Conference Papers

2025

AAAI 2025

• Auditing and Enforcing Conditional Fairness via Optimal Transport.
• Bias Unveiled: Investigating Social Bias in LLM-Generated Code.
• Can Private Machine Learning Be Fair?
• Causal Prompting: Debiasing Large Language Model Prompting Based on Front-Door Adjustment.
• Constructing Fair Latent Space for Intersection of Fairness and Explainability.
• CUGF: A Reliable and Fair Recommendation Framework.
• Disentangling, Amplifying, and Debiasing: Learning Disentangled Representations for Fair Graph Neural Networks.
• Equal Merit Does Not Imply Equality: Discrimination at Equilibrium in a Hiring Market with Symmetric Agents.
• Exploring and Mitigating Implicit Bias in Large Language Models: A Cross-Domain Evaluation Framework.
• Extracting PAC Decision Trees from Black Box Binary Classifiers: The Gender Bias Study Case on BERT-based Language Models.
• Fair Federated Survival Analysis.
• Fair Graph U-Net: A Fair Graph Learning Framework Integrating Group and Individual Awareness.
• Fair Text-to-Image Diffusion via Fair Mapping.
• Fair Training with Zero Inputs.
• FairGP: A Scalable and Fair Graph Transformer Using Graph Partitioning.
• Fairness Issues and Mitigations in (Differentially Private) Socio-Demographic Data Processes.
• Fairness Shields: Safeguarding against Biased Decision Makers.
• Fairness-Accuracy Trade-Offs: A Causal Perspective.
• FedAA: A Reinforcement Learning Perspective on Adaptive Aggregation for Fair and Robust Federated Learning.
• FROC: Building Fair ROC from a Trained Classifier.
• Improved Fixed-Parameter Bounds for Min-Sum-Radii and Diameters k-Clustering and Their Fair Variants.
• Investigating and Mitigating Undesirable Biases in Large Language Models.
• Local Causal Discovery for Structural Evidence of Direct Discrimination.
• LoGoFair: Post-Processing for Local and Global Fairness in Federated Learning.
• Maintaining Fairness in Logit-based Knowledge Distillation for Class-Incremental Learning.
• Metric-Agnostic Continual Learning for Sustainable Group Fairness.
• Mitigating Social Bias in Large Language Models: A Multi-Objective Approach Within a Multi-Agent Framework.
• Mjölnir: Breaking the Shield of Perturbation-Protected Gradients via Adaptive Diffusion.
• Multi-OphthaLingua: A Multilingual Benchmark for Assessing and Debiasing LLM Ophthalmological QA in LMICs.
• Navigating Towards Fairness with Data Selection.
• Privacy, Utility and Fairness: Navigating Trade-offs in Differentially Private Machine Learning.
• Searching for Unfairness in Algorithms’ Outputs: Novel Tests and Insights.
• Sequential Conditional Transport on Probabilistic Graphs for Interpretable Counterfactual Fairness.
• Thinking Racial Bias in Fair Forgery Detection: Models, Datasets and Evaluations.
• Who’s the (Multi-)Fairest of Them All: Rethinking Interpolation-Based Data Augmentation Through the Lens of Multicalibration.

AIES 2025

• Advancing Early Alzheimer’s Disease Detection in Underdeveloped Areas with Fair Explainable AI Methods.
• Schools of AI in the Public Sector: Fairness and Accountability Concerns.
• The Need For Inclusive NLP: Addressing Sociodemographic Bias and Enhancing Sociotechnical Systems through Interdisciplinary Frameworks.
• Uncovering Gender Biases in Human-AI Platforms.

AISTATS 2025

• A Subquadratic Time Approximation Algorithm for Individually Fair k-Center.
• Advancing Fairness in Precision Medicine: A Universal Framework for Optimal Treatment Estimation in Censored Data.
• Differentially Private Graph Data Release: Inefficiencies & Unfairness.
• Fairness Risks for Group-Conditionally Missing Demographics.
• Global Group Fairness in Federated Learning via Function Tracking.
• HR-Bandit: Human-AI Collaborated Linear Recourse Bandit.
• Post-processing for Fair Regression via Explainable SVD.
• Robust Fair Clustering with Group Membership Uncertainty Sets.
• The cost of local and global fairness in Federated Learning.
• To Give or Not to Give? The Impacts of Strategically Withheld Recourse.
• Towards Fair Graph Learning without Demographic Information.

BIGDATA 2025

• A Deep Latent Factor Graph Clustering with Fairness-Utility Trade-Off Perspective.
• A Prototype-Based Personalized and Fair Federated Graph Learning Algorithm.
• Adversarial Bias: Data Poisoning Attacks on Fairness.
• Cognitive Error Correction Prompting for Effective Social Bias Mitigation in Large Language Models.
• FABLE: Fairness Attack in Abusive Language Detection.
• Fair In-Context Learning via Latent Concept Variables.
• Investigating the Impact of Japanese Names and Japanese Prompts on Social Bias in Hiring Decisions Using LLMs.
• Mapping Discrimination in LLM-Driven HR Systems.
• Performance Gap-Aware Distributionally Robust Optimization for Fair Deep Knowledge Tracing.
• Quantifying and Mitigating Occupational Bias in Open-Source Large Language Models.

CIKM 2025

• CondFairGen A Fair Conditional Generator for Tabular Data via Adaptive Sampling.
• Eliminating Bias from Presentation Attack Detection Algorithms for Face Recognition Systems.
• Eliminating Sentiment Bias in Recommender Systems by Counterfactual Inference.
• Enabling Group Fairness in Machine Unlearning via Distribution Correction.
• Evaluating and Addressing Fairness Across User Groups in Negative Sampling for Recommender Systems.
• Exploring Causal Effect of Social Bias on Faithfulness Hallucinations in Large Language Models.
• FairAD: Computationally Efficient Fair Graph Clustering via Algebraic Distance.
• Fairness in Language Models: A Tutorial.
• FairRegBoost: An End-to-End Data Processing Framework for Fair and Scalable Regression.
• FairSplit: Mitigating Bias in Graph Neural Networks through Sensitivity-based Edge Partitioning.
• FedFMD: Fairness-Driven Adaptive Aggregation in Federated Learning via Mahalanobis Distance.
• FnRGNN: Distribution-aware Fairness in Graph Neural Network.
• FROG: Fair Removal on Graph.
• Improving Content Anomaly Detection on Social Media via Counterfactual Mitigation of Social Event-Induced Bias.
• Improving Recommendation Fairness via Graph Structure and Representation Augmentation.
• JustEva: A Toolkit to Evaluate LLM Fairness in Legal Knowledge Inference.
• LLMCE: Adapting LLMs with Adversarial Debiasing for Counterfactual Estimation over Time.
• MMFair: Fair Learning via Min-Min Optimization.
• MMM-fair: An Interactive Toolkit for Exploring and Operationalizing Multi-Fairness Trade-offs.
• More Women, Same Stereotypes: Unpacking the Gender Bias Paradox in Large Language Models.
• Revisiting Pre-processing Group Fairness: A Modular Benchmarking Framework.
• T-Retrievability: A Topic-Focused Approach to Measure Fair Document Exposure in Information Retrieval.
• terazi: AI Fairness Tool for Doubly Imbalanced Data.
• Towards Understanding Bias in Synthetic Data for Evaluation.
• When Variety Seeking Meets Multi-Sided Recommendation Fairness: A Consistent and Personalized Multi-Objective Optimization Framework.

FAT* 2025

• “Since Lawyers are Males..”: Examining Implicit Gender Bias in Hindi Language Generation by LLMs.
• A Framework for Auditing Chatbots for Dialect-Based Quality-of-Service Harms.
• Actions Speak Louder than Words: Agent Decisions Reveal Implicit Biases in Language Models.
• Adultification Bias in LLMs and Text-to-Image Models.
• Aggregating Concepts of Fairness and Accuracy in Prediction Algorithms.
• AI constructs gendered struggle narratives: Implications for self-concept and systems design.
• Algorithmic Fairness, Decision Thresholds, and the Separateness of Persons.
• Applying Data Feminism Principles to Assess Bias in English and Arabic NLP Research.
• Auditing for Bias in Ad Delivery Using Inferred Demographic Attributes.
• Beyond Consistency: Nuanced Metrics for Individual Fairness.
• Beyond Semantics: Examining Gender Bias in LLMs Deployed within Low-resource Contexts in India.
• Characterizing Bias: Benchmarking Large Language Models in Simplified versus Traditional Chinese.
• Characterizing the Default Persona During Design: Mental Representations of Technology Users are Gendered.
• Component-Based Fairness in Face Attribute Classification with Bayesian Network-informed Meta Learning.
• Dangerous Criminals and Beautiful Prostitutes? Investigating Harmful Representations in Dutch Language Models.
• Detecting Linguistic Indicators for Stereotype Assessment with Large Language Models.
• Detecting Prefix Bias in LLM-based Reward Models.
• Discrimination Exposed? On the Reliability of Explanations for Discrimination Detection.
• Discrimination Induced by Algorithmic Recourse Objectives.
• External Evaluation of Discrimination Mitigation Efforts in Meta’s Ad Delivery.
• FADE: Federated Aggregation with Discrimination Elimination.
• Fairness Beyond the Algorithmic Frame: Actionable Recommendations for an Intersectional Approach.
• Fairness of Deep Ensembles: On the interplay between per-group task difficulty and under-representation.
• Fairness-Guided Pruning of Decision Trees.
• FairTranslate: an English-French Dataset for Gender Bias Evaluation in Machine Translation by Overcoming Gender Binarity.
• Formalising Anti-Discrimination Law in Automated Decision Systems.
• Gender Bias in Explainability: Investigating Performance Disparity in Post-hoc Methods.
• Gender Trouble in Language Models: An Empirical Audit Guided by Gender Performativity Theory.
• Gone With the Bits: Revealing Racial Bias in Low-Rate Neural Compression for Facial Images.
• Group Fair Rated Preference Aggregation: Ties Are (Mostly) All You Need.
• Historical Methods for AI Evaluations, Assessments, and Audits.
• How Do Users Identify and Perceive Stereotypes? Understanding User Perspectives on Stereotypical Biases in Large Language Models.
• hyperFA*IR: A hypergeometric approach to fair rankings with finite candidate pool.
• Identities are not Interchangeable: The Problem of Overgeneralization in Fair Machine Learning.
• Is It Fair Enough? Supporting Equitable Group Work Assignment with Work Division Dashboard.
• It’s only fair when I think it’s fair: How Gender Bias Alignment Undermines Distributive Fairness in Human-AI Collaboration.
• Justified Evidence Collection for Argument-based AI Fairness Assurance.
• LEAD: Towards Learning-Based Equity-Aware Decarbonization in Ridesharing Platforms.
• M²FGB: A Min-Max Gradient Boosting Framework for Subgroup Fairness.
• Measuring Machine Learning Harms from Stereotypes Requires Understanding Who Is Harmed by Which Errors in What Ways.
• Not Even Nice Work If You Can Get It; A Longitudinal Study of Uber’s Algorithmic Pay and Pricing.
• Not Like Us, Hunty: Measuring Perceptions and Behavioral Effects of Minoritized Anthropomorphic Cues in LLMs.
• Oh the Prices You’ll See: Designing a Fair Exchange System to Mitigate Personalized Pricing.
• Position is Power: System Prompts as a Mechanism of Bias in Large Language Models (LLMs).
• Pragmatic Fairness: Evaluating ML Fairness Within the Constraints of Industry.
• Randomness, Not Representation: The Unreliability of Evaluating Cultural Alignment in LLMs.
• Recourse, Repair, Reparation, & Prevention: A Stakeholder Analysis of AI Supply Chains.
• SHAP-based Explanations are Sensitive to Feature Representation.
• Social Bias in Vision Transformers: A Comparative Study Across Architectures and Learning Paradigms.
• Social Perception of Faces in a Vision-Language Model.
• The Problem of Generics in LLM Training.
• The Root Shapes the Fruit: On the Persistence of Gender-Exclusive Harms in Aligned Language Models.
• TIDEs: A Transgender and Nonbinary Community-Labeled Dataset and Model for Transphobia Identification in Digital Environments.
• Time Can Invalidate Algorithmic Recourse.
• Towards Effective Discrimination Testing for Generative AI.
• Uncovering the Linguistic Roots of Bias: Insights and Mitigation in Large Language Models.
• Understanding Gen Alpha’s Digital Language: Evaluation of LLM Safety Systems for Content Moderation.
• Understanding Gender Bias in AI-Generated Product Descriptions.
• WEIRD Audits? Research Trends, Linguistic and Geographical Disparities in the Algorithm Audits of Online Platforms - A Systematic Literature Review.
• When Collaborative Filtering is not Collaborative: Unfairness of PCA for Recommendations.
• When Testing AI Tests Us: Safeguarding Mental Health on the Digital Frontlines.
• Who Gets Heard? Calling Out the “Hard-to-Reach” Myth for Non-WEIRD Populations’ Recruitment and Involvement in Research.

ICDM 2025

• FAIM-RL: A Reinforcement Learning Approach for Fairness-Aware Adaptive Influence Maximization.
• fair-LDP: Uncertainty-Guided Fairness and Privacy for Federated Healthcare Learning.
• Modularity-Fair Deep Community Detection.
• TabFairGDT: A Fast Fair Tabular Data Generator Using Autoregressive Decision Trees.

ICLR 2025

• A Causal Lens for Learning Long-term Fair Policies.
• A Generic Framework for Conformal Fairness.
• Adversarial Latent Feature Augmentation for Fairness.
• Bridging Jensen Gap for Max-Min Group Fairness Optimization in Recommendation.
• CEB: Compositional Evaluation Benchmark for Fairness in Large Language Models.
• Certifying Counterfactual Bias in LLMs.
• Collapsed Language Models Promote Fairness.
• Conformal Prediction Sets Can Cause Disparate Impact.
• CPSample: Classifier Protected Sampling for Guarding Training Data During Diffusion.
• Enhancing Robust Fairness via Confusional Spectral Regularization.
• Examining Alignment of Large Language Models through Representative Heuristics: the case of political stereotypes.
• Fair Clustering in the Sliding Window Model.
• Fair Submodular Cover.
• FairDen: Fair Density-Based Clustering.
• FairMT-Bench: Benchmarking Fairness for Multi-turn Dialogue in Conversational LLMs.
• Feature Responsiveness Scores: Model-Agnostic Explanations for Recourse.
• First-Person Fairness in Chatbots.
• From Search to Sampling: Generative Models for Robust Algorithmic Recourse.
• Justice or Prejudice? Quantifying Biases in LLM-as-a-Judge.
• No Location Left Behind: Measuring and Improving the Fairness of Implicit Representations for Earth Data.
• OASIS Uncovers: High-Quality T2I Models, Same Old Stereotypes.
• PFGuard: A Generative Framework with Privacy and Fairness Safeguards.
• Polyrating: A Cost-Effective and Bias-Aware Rating System for LLM Evaluation.
• Prompting Fairness: Integrating Causality to Debias Large Language Models.
• Relax and Merge: A Simple Yet Effective Framework for Solving Fair k-Means and k-sparse Wasserstein Barycenter Problems.
• Rethinking Fair Representation Learning for Performance-Sensitive Tasks.
• Revealing and Reducing Gender Biases in Vision and Language Assistants (VLAs).
• SEBRA : Debiasing through Self-Guided Bias Ranking.
• Swiss Army Knife: Synergizing Biases in Knowledge from Vision Foundation Models for Multi-Task Learning.
• Towards counterfactual fairness through auxiliary variables.
• Towards Marginal Fairness Sliced Wasserstein Barycenter.
• Towards Understanding Text Hallucination of Diffusion Models via Local Generation Bias.
• Two Effects, One Trigger: On the Modality Gap, Object Bias, and Information Imbalance in Contrastive Vision-Language Models.

ICML 2025

• Accelerating Spectral Clustering under Fairness Constraints.
• Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks.
• Algorithmic Recourse for Long-Term Improvement.
• Almost Optimal Fully Dynamic k-Center Clustering with Recourse.
• An Analysis for Reasoning Bias of Language Models with Small Initialization.
• B-score: Detecting biases in large language models using response history.
• BiAssemble: Learning Collaborative Affordance for Bimanual Geometric Assembly.
• Bridging Fairness and Efficiency in Conformal Inference: A Surrogate-Assisted Group-Clustered Approach.
• Cannot See the Forest for the Trees: Invoking Heuristics and Biases to Elicit Irrational Choices of LLMs.
• Causal Logistic Bandits with Counterfactual Fairness Constraints.
• COMRECGC: Global Graph Counterfactual Explainer through Common Recourse.
• Counterfactual Voting Adjustment for Quality Assessment and Fairer Voting in Online Platforms with Helpfulness Evaluation.
• Disparate Conditional Prediction in Multiclass Classifiers.
• Distribution-aware Fairness Learning in Medical Image Segmentation From A Control-Theoretic Perspective.
• Efficient Source-free Unlearning via Energy-Guided Data Synthesis and Discrimination-Aware Multitask Optimization.
• FACTER: Fairness-Aware Conformal Thresholding and Prompt Engineering for Enabling Fair LLM-Based Recommender Systems.
• Fair Clustering via Alignment.
• FairICP: Encouraging Equalized Odds via Inverse Conditional Permutation.
• Fairness on Principal Stratum: A New Perspective on Counterfactual Fairness.
• Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective.
• FairPFN: A Tabular Foundation Model for Causal Fairness.
• FDGen: A Fairness-Aware Graph Generation Model.
• Finding Wasserstein Ball Center: Efficient Algorithm and The Applications in Fairness.
• GS-Bias: Global-Spatial Bias Learner for Single-Image Test-Time Adaptation of Vision-Language Models.
• Intersectional Fairness in Reinforcement Learning with Large State and Constraint Spaces.
• Is Your Model Fairly Certain? Uncertainty-Aware Fairness Evaluation for LLMs.
• On the Alignment between Fairness and Accuracy: from the Perspective of Adversarial Robustness.
• Optimal Algorithm for Max-Min Fair Bandit.
• Optimal Fair Learning Robust to Adversarial Distribution Shift.
• Preserving AUC Fairness in Learning with Noisy Protected Groups.
• Relative Error Fair Clustering in the Weak-Strong Oracle Model.
• Rethinking the Bias of Foundation Model under Long-tailed Distribution.
• Stable Fair Graph Representation Learning with Lipschitz Constraint.
• The Disparate Benefits of Deep Ensembles.
• Theoretically Unmasking Inference Attacks Against LDP-Protected Clients in Federated Vision Models.
• Two Tickets are Better than One: Fair and Accurate Hiring Under Strategic LLM Manipulations.
• Understanding the Unfairness in Network Quantization.
• You Get What You Give: Reciprocally Fair Federated Learning.

IJCAI 2025

• Agent-based Modeling for Policy-Making in Inequity Contexts.
• CABIN: Debiasing Vision-Language Models Using Backdoor Adjustments.
• Causality-Inspired Disentanglement for Fair Graph Neural Networks.
• Constrained Serial Dictatorships Can Be Fair.
• DECASTE: Unveiling Caste Stereotypes in Large Language Models Through Multi-Dimensional Bias Analysis.
• DFCA: Disentangled Feature Contrastive Learning and Augmentation for Fairer Dermatological Diagnostics.
• Dividing Conflicting Items Fairly.
• Efficient Counterexample-Guided Fairness Verification and Repair of Neural Networks Using Satisfiability Modulo Convex Programming.
• Ensuring Reliable and Transparent Algorithmic Fairness Through Optimal Transport and Uncertainty Quantification.
• Evaluating and Mitigating Linguistic Discrimination in Large Language Models: Perspectives on Safety Equity and Knowledge Equity.
• Exploring Equity of Climate Policies Using Multi-Agent Multi-Objective Reinforcement Learning.
• FADE: Towards Fairness-aware Data Generation for Domain Generalization via Classifier-Guided Score-based Diffusion Models.
• Fair Incomplete Multi-View Clustering via Distribution Alignment.
• Fair Submodular Maximization over a Knapsack Constraint.
• FairCognizer: A Model for Accurate Predictions with Inherent Fairness Evaluation (Extended Abstract).
• fairGNN-WOD: Fair Graph Learning Without Complete Demographics.
• Fairness-Aware Interactive Target Variable Definition.
• FairSMOE: Mitigating Multi-Attribute Fairness Problem with Sparse Mixture-of-Experts.
• Federated Learning at the Forefront of Fairness: A Multifaceted Perspective.
• HCRide: Harmonizing Passenger Fairness and Driver Preference for Human-Centered Ride-Hailing.
• HIPP: Protecting Image Privacy via High-Quality Reversible Protected Version.
• HPDM: A Hierarchical Popularity-aware Debiased Modeling Approach for Personalized News Recommender.
• Improved Rank Aggregation Under Fairness Constraint.
• Interaction-Data-guided Conditional Instrumental Variables for Debiasing Recommender Systems.
• Learning Causally Disentangled Representations for Fair Personality Detection.
• Learning Heterogeneous Performance-Fairness Trade-offs in Federated Learning.
• Logarithmic Approximations for Fair k-Set Selection.
• Measuring and Mitigating Homelessness Bias: Leveraging AI for Social Impact.
• NeuBM: Mitigating Model Bias in Graph Neural Networks Through Neutral Input Calibration.
• Never Train from Scratch: Fair Comparison of Long-Sequence Models Requires Data-Driven Priors (Extended Abstract).
• On the Discrimination and Consistency for Exemplar-Free Class Incremental Learning.
• Parameterized Approximation Algorithm for Doubly Constrained Fair Clustering.
• Towards Fairness with Limited Demographics via Disentangled Learning.
• What is Behind Homelessness Bias? Using LLMs and NLP to Mitigate Homelessness by Acting on Social Stigma.
• Wisdom from Diversity: Bias Mitigation Through Hybrid Human-LLM Crowds.

KDD 2025

Main conference proceedings are split across part 1 and part 2.

• Addressing Correlated Latent Exogenous Variables in Debiased Recommender Systems.
• Counterfactual Fairness Through Transforming Data Orthogonal to Bias.
• Disclosing Actual Controller based on Equity Knowledge Graph Learning.
• Fair Diversity Maximization with Few Representatives.
• Fair Set Cover.
• FairCDR: Transferring Fairness and User Preferences for Cross-Domain Recommendation.
• Fairness without Demographics through Learning Graph of Gradients.
• Fairness-Aware Graph Learning: A Benchmark.
• Monitoring Robustness and Individual Fairness.
• Pairwise Sample Complexity for Fair Active Ranking with Cascaded Norm Objectives.
• PDMC: Generating Feasible Algorithmic Recourse via Perturbation Data Manifold Constraint.
• PraFFL: A Preference-Aware Scheme in Fair Federated Learning.
• Taming Recommendation Bias with Causal Intervention on Evolving Personal Popularity.
• Towards Collaborative Fairness in Federated Learning Under Imbalanced Covariate Shift.
• Towards Controllable Hybrid Fairness in Graph Neural Networks.
• Track and Tweak: Monitoring and Improving Group Fairness for Temporal Graph Neural Networks in Real Time.

NIPS 2025

dblp main conference page not available as of May 11, 2026

SDM 2025

• Unveiling the Impact of Local Homophily on GNN Fairness: In-Depth Analysis and New Benchmarks.

UAI 2025

• Federated Rényi Fair Inference in Federated Heterogeneous System.

WWW 2025

• Bridging Fairness and Uncertainty: Theoretical Insights and Practical Strategies for Equalized Coverage in GNNs.
• Detecting Linguistic Bias in Government Documents Using Large language Models.
• Fair Clustering for Data Summarization: Improved Approximation Algorithms and Complexity Insights.
• Fair Network Communities through Group Modularity.
• Fair Personalized Learner Modeling Without Sensitive Attributes.
• Fairness Evaluation with Item Response Theory.
• Fairness-aware Prompt Tuning for Graph Neural Networks.
• Generating with Fairness: A Modality-Diffused Counterfactual Framework for Incomplete Multimodal Recommendations.
• Joint Evaluation of Fairness and Relevance in Recommender Systems with Pareto Frontier.
• Learning Feasible Causal Algorithmic Recourse: A Prior Structural Knowledge Free Approach.
• Reducing Symbiosis Bias through Better A/B Tests of Recommendation Algorithms.
• Social Bots Meet Large Language Model: Political Bias and Social Learning Inspired Mitigation Strategies.
• SPRec: Self-Play to Debias LLM-based Recommendation.
• Unmasking Gender Bias in Recommendation Systems and Enhancing Category-Aware Fairness.
• What’s in a Query: Polarity-Aware Distribution-Based Fair Ranking.

Others 2025

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WSDM 2025

• Combating Heterogeneous Model Biases in Recommendations via Boosting.
• How Do Recommendation Models Amplify Popularity Bias? An Analysis from the Spectral Perspective.
• Integrating Knowledge Graphs and Neuro-Symbolic AI: LDM Enables FAIR and Federated Research Data Management.
• Privacy-Preserving Orthogonal Aggregation for Guaranteeing Gender Fairness in Federated Recommendation.
• Unifying Bias and Unfairness in Information Retrieval: New Challenges in the LLM Era.
• Writing Style Matters: An Examination of Bias and Fairness in Information Retrieval Systems.

2024

AAAI 2024

• A Sequentially Fair Mechanism for Multiple Sensitive Attributes.
• A Simple and Practical Method for Reducing the Disparate Impact of Differential Privacy.
• Adversarial Fairness Network.
• All Should Be Equal in the Eyes of LMs: Counterfactually Aware Fair Text Generation.
• An Information-Flow Perspective on Algorithmic Fairness.
• Arbitrariness and Social Prediction: The Confounding Role of Variance in Fair Classification.
• Backdoor Adjustment via Group Adaptation for Debiased Coupon Recommendations.
• Biases Mitigation and Expressiveness Preservation in Language Models: A Comprehensive Pipeline (Student Abstract).
• Causal Adversarial Perturbations for Individual Fairness and Robustness in Heterogeneous Data Spaces.
• Chasing Fairness in Graphs: A GNN Architecture Perspective.
• Class-Attribute Priors: Adapting Optimization to Heterogeneity and Fairness Objective.
• Demystifying Algorithmic Fairness in an Uncertain World.
• Equity-Transformer: Solving NP-Hard Min-Max Routing Problems as Sequential Generation with Equity Context.
• Evaluating the Efficacy of Prompting Techniques for Debiasing Language Model Outputs (Student Abstract).
• Fair and Optimal Prediction via Post-Processing.
• Fair Multivariate Adaptive Regression Splines for Ensuring Equity and Transparency.
• Fair Participation via Sequential Policies.
• Fair Representation Learning with Maximum Mean Discrepancy Distance Constraint (Student Abstract).
• Fair Sampling in Diffusion Models through Switching Mechanism.
• FAIR-FER: A Latent Alignment Approach for Mitigating Bias in Facial Expression Recognition (Student Abstract).
• Fairness under Covariate Shift: Improving Fairness-Accuracy Tradeoff with Few Unlabeled Test Samples.
• Fairness with Censorship: Bridging the Gap between Fairness Research and Real-World Deployment.
• Fairness without Demographics through Shared Latent Space-Based Debiasing.
• Fairness-Aware Structured Pruning in Transformers.
• FairPlay: A Multi-Sided Fair Dynamic Pricing Policy for Hotels.
• FairSIN: Achieving Fairness in Graph Neural Networks through Sensitive Information Neutralization.
• FairTrade: Achieving Pareto-Optimal Trade-Offs between Balanced Accuracy and Fairness in Federated Learning.
• FairWASP: Fast and Optimal Fair Wasserstein Pre-processing.
• FedGCR: Achieving Performance and Fairness for Federated Learning with Distinct Client Types via Group Customization and Reweighting.
• FedLF: Layer-Wise Fair Federated Learning.
• Generating Diagnostic and Actionable Explanations for Fair Graph Neural Networks.
• ImageCaptioner2: Image Captioner for Image Captioning Bias Amplification Assessment.
• Intra- and Inter-group Optimal Transport for User-Oriented Fairness in Recommender Systems.
• Learning Fair Policies for Multi-Stage Selection Problems from Observational Data.
• Leveraging Diffusion Perturbations for Measuring Fairness in Computer Vision.
• Leveraging Opposite Gender Interaction Ratio as a Path towards Fairness in Online Dating Recommendations Based on User Sexual Orientation.
• Long-Term Fair Decision Making through Deep Generative Models.
• Mitigating Label Bias in Machine Learning: Fairness through Confident Learning.
• Moderate Message Passing Improves Calibration: A Universal Way to Mitigate Confidence Bias in Graph Neural Networks.
• Multi-Dimensional Fair Federated Learning.
• No Prejudice! Fair Federated Graph Neural Networks for Personalized Recommendation.
• Online Restless Multi-Armed Bandits with Long-Term Fairness Constraints.
• Project-Fair and Truthful Mechanisms for Budget Aggregation.
• Promoting Fair Vaccination Strategies through Influence Maximization: A Case Study on COVID-19 Spread.
• Providing Fair Recourse over Plausible Groups.
• Referee-Meta-Learning for Fast Adaptation of Locational Fairness.
• Responsible Bandit Learning via Privacy-Protected Mean-Volatility Utility.
• Robust Evaluation Measures for Evaluating Social Biases in Masked Language Models.
• Robust Few-Shot Named Entity Recognition with Boundary Discrimination and Correlation Purification.
• SafeAR: Safe Algorithmic Recourse by Risk-Aware Policies.
• SimFair: Physics-Guided Fairness-Aware Learning with Simulation Models.
• Strategyproof Mechanisms for Group-Fair Obnoxious Facility Location Problems.
• Temporal Fairness in Multiwinner Voting.
• The Fairness Fair: Bringing Human Perception into Collective Decision-Making.
• Towards a Theoretical Understanding of Why Local Search Works for Clustering with Fair-Center Representation.
• Towards Fair Graph Federated Learning via Incentive Mechanisms.
• Towards Fairer Centroids in K-means Clustering.
• Towards Fairness in Online Service with K Servers and Its Application on Fair Food Delivery.
• Transforming Healthcare: A Comprehensive Approach to Mitigating Bias and Fostering Empathy through AI-Driven Augmented Reality.
• Unveiling the Tapestry of Automated Essay Scoring: A Comprehensive Investigation of Accuracy, Fairness, and Generalizability.

AIES 2024

• “I Don’t See Myself Represented Here at All”: User Experiences of Stable Diffusion Outputs Containing Representational Harms across Gender Identities and Nationalities.
• A Causal Framework to Evaluate Racial Bias in Law Enforcement Systems.
• A Human-in-the-Loop Fairness-Aware Model Selection Framework for Complex Fairness Objective Landscapes.
• Algorithm-Assisted Decision Making and Racial Disparities in Housing: A Study of the Allegheny Housing Assessment Tool.
• Algorithmic Fairness From the Perspective of Legal Anti-discrimination Principles.
• Automate or Assist? The Role of Computational Models in Identifying Gendered Discourse in US Capital Trial Transcripts.
• Breaking Bias, Building Bridges: Evaluation and Mitigation of Social Biases in LLMs via Contact Hypothesis.
• Breaking the Global North Stereotype: A Global South-centric Benchmark Dataset for Auditing and Mitigating Biases in Facial Recognition Systems.
• Dataset Scale and Societal Consistency Mediate Facial Impression Bias in Vision-Language AI.
• Face the Facts: Using Face Averaging to Visualize Gender-by-Race Bias in Facial Analysis Algorithms.
• Fairness in Reinforcement Learning: A Survey.
• Foundations for Unfairness in Anomaly Detection - Case Studies in Facial Imaging Data.
• Gender in Pixels: Pathways to Non-binary Representation in Computer Vision.
• Gender, Race, and Intersectional Bias in Resume Screening via Language Model Retrieval.
• Hidden or Inferred: Fair Learning-To-Rank With Unknown Demographics.
• How Are LLMs Mitigating Stereotyping Harms? Learning from Search Engine Studies.
• Identifying Implicit Social Biases in Vision-Language Models.
• Individual Fairness in Graphs Using Local and Global Structural Information.
• Interpretations, Representations, and Stereotypes of Caste within Text-to-Image Generators.
• Legal Minds, Algorithmic Decisions: How LLMs Apply Constitutional Principles in Complex Scenarios.
• LLM Voting: Human Choices and AI Collective Decision-Making.
• Misrepresented Technological Solutions in Imagined Futures: The Origins and Dangers of AI Hype in the Research Community.
• Mitigating Urban-Rural Disparities in Contrastive Representation Learning with Satellite Imagery.
• Nothing Comes Without Its World - Practical Challenges of Aligning LLMs to Situated Human Values through RLHF.
• Observing Context Improves Disparity Estimation when Race is Unobserved.
• PoliTune: Analyzing the Impact of Data Selection and Fine-Tuning on Economic and Political Biases in Large Language Models.
• Proxy Fairness under the European Data Protection Regulation and the AI Act: A Perspective of Sensitivity and Necessity.
• Racial and Neighborhood Disparities in Legal Financial Obligations in Jefferson County, Alabama.
• Reducing Biases towards Minoritized Populations in Medical Curricular Content via Artificial Intelligence for Fairer Health Outcomes.
• Representation Magnitude Has a Liability to Privacy Vulnerability.
• SoUnD Framework: Analyzing (So)cial Representation in (Un)structured (D)ata.
• Stable Diffusion Exposed: Gender Bias from Prompt to Image.
• Trustworthy Social Bias Measurement.
• Understanding Intrinsic Socioeconomic Biases in Large Language Models.
• What’s Distributive Justice Got to Do with It? Rethinking Algorithmic Fairness from a Perspective of Approximate Justice.

AISTATS 2024

• A Scalable Algorithm for Individually Fair k-Means Clustering.
• Achieving Fairness through Separability: A Unified Framework for Fair Representation Learning.
• Achieving Group Distributional Robustness and Minimax Group Fairness with Interpolating Classifiers.
• Auditing Fairness under Unobserved Confounding.
• Fair k-center Clustering with Outliers.
• Fair Machine Unlearning: Data Removal while Mitigating Disparities.
• Fair Soft Clustering.
• Fair Supervised Learning with A Simple Random Sampler of Sensitive Attributes.
• Fairness in Submodular Maximization over a Matroid Constraint.
• FairRR: Pre-Processing for Group Fairness through Randomized Response.
• Imposing Fairness Constraints in Synthetic Data Generation.
• On the Vulnerability of Fairness Constrained Learning to Malicious Noise.
• The Risks of Recourse in Binary Classification.
• To Pool or Not To Pool: Analyzing the Regularizing Effects of Group-Fair Training on Shared Models.
• Uncertainty Matters: Stable Conclusions under Unstable Assessment of Fairness Results.

BIGDATA 2024

• A Beam-Search Based Method to Select Classification and Imputation Methods for Fair and Accurate Data Analysis.
• Adversarially Exploring Vulnerabilities in LLMs to Evaluate Social Biases.
• Algorithmic Lending Bias: Evaluating the Fairness of Historical Redlining in Loan Approvals.
• Challenging Fairness: A Comprehensive Exploration of Bias in LLM-Based Recommendations.
• Correcting Systematic Bias in LLM-Generated Dialogues Using Big Five Personality Traits.
• Debias-CLR: A Contrastive Learning Based Debiasing Method for Algorithmic Fairness in Healthcare Applications.
• Decoding Narratives: Towards a Classification Analysis for Stereotypical Patterns in Italian News Headlines.
• Enhancing Fairness in Medical Image Classification: A Comparative Study of Convolutional Neural Networks and Adversarial Learning.
• Enhancing Predictive Fairness in Deep Knowledge Tracing with Sequence Inverted Data Augmentation.
• Evaluating Company-specific Biases in Financial Sentiment Analysis using Large Language Models.
• Evaluating Fairness of Mask R-CNN for Kidney Infection Detection based on Renal Scintigraphy.
• FAIR Digital Objects for the Realization of Globally Aligned Data Spaces.
• FairLENS: Assessing Fairness in Law Enforcement Speech Recognition.
• Fairness in Monotone k-submodular Maximization: Algorithms and Applications.
• Federated Learning for Medical Applications - A Study on Performance and Bias with Logistic Regression on Small Datasets.
• FEED: Fairness-Enhanced Meta-Learning for Domain Generalization.
• Leveraging LLMs for Fair Data Labeling and Validation in Crowdsourcing Environments [Vision Paper].
• Meta-Learning for Debiasing Recommendation using Simulated Uniform Data.
• Problematic Tokens: Tokenizer Bias in Large Language Models.
• Training Fair Models in Federated Learning without Data Privacy Infringement.

CIKM 2024

• A Self-Adaptive Fairness Constraint Framework for Industrial Recommender System.
• Contrastive Disentangled Representation Learning for Debiasing Recommendation with Uniform Data.
• FaDE: A Face Segment Driven Identity Anonymization Framework For Fair Face Recognition.
• Fairness in Large Language Models in Three Hours.
• FairRankTune: A Python Toolkit for Fair Ranking Tasks.
• Guaranteeing Accuracy and Fairness under Fluctuating User Traffic: A Bankruptcy-Inspired Re-ranking Approach.
• Integrating Fair Representation Learning with Fairness Regularization for Intersectional Group Fairness.
• Learning Fair Invariant Representations under Covariate and Correlation Shifts Simultaneously.
• Mitigating Exposure Bias in Online Learning to Rank Recommendation: A Novel Reward Model for Cascading Bandits.
• On the Sensitivity of Individual Fairness: Measures and Robust Algorithms.
• TEXT CAN BE FAIR: Mitigating Popularity Bias with PLMs by Learning Relative Preference.
• The Elusiveness of Detecting Political Bias in Language Models.
• Towards Fair Graph Anomaly Detection: Problem, Benchmark Datasets, and Evaluation.
• Wise Fusion: Group Fairness Enhanced Rank Fusion.

FAT* 2024

• A Critical Survey on Fairness Benefits of Explainable AI.
• A Framework for Assurance Audits of Algorithmic Systems.
• Actionable Recourse for Automated Decisions: Examining the Effects of Counterfactual Explanation Type and Presentation on Lay User Understanding.
• Akal Badi ya Bias: An Exploratory Study of Gender Bias in Hindi Language Technology.
• Algorithmic Arbitrariness in Content Moderation.
• Algorithmic Fairness in Performative Policy Learning: Escaping the Impossibility of Group Fairness.
• Algorithmic Misjudgement in Google Search Results: Evidence from Auditing the US Online Electoral Information Environment.
• Algorithmic Pluralism: A Structural Approach To Equal Opportunity.
• Analyzing the Relationship Between Difference and Ratio-Based Fairness Metrics.
• Auditing for Racial Discrimination in the Delivery of Education Ads.
• BaBE: Enhancing Fairness via Estimation of Explaining Variables.
• Balancing Act: Evaluating People’s Perceptions of Fair Ranking Metrics.
• Benchmarking the Fairness of Image Upsampling Methods.
• Beyond Behaviorist Representational Harms: A Plan for Measurement and Mitigation.
• Black-Box Access is Insufficient for Rigorous AI Audits.
• CARMA: A practical framework to generate recommendations for causal algorithmic recourse at scale.
• Designing Long-term Group Fair Policies in Dynamical Systems.
• Diversity of What? On the Different Conceptualizations of Diversity in Recommender Systems.
• Equalised Odds is not Equal Individual Odds: Post-processing for Group and Individual Fairness.
• Ethnic Classifications in Algorithmic Fairness: Concepts, Measures and Implications in Practice.
• Evidence of What, for Whom? The Socially Contested Role of Algorithmic Bias in a Predictive Policing Tool.
• Explainable Artificial Intelligence for Academic Performance Prediction. An Experimental Study on the Impact of Accuracy and Simplicity of Decision Trees on Causability and Fairness Perceptions.
• Fairness Feedback Loops: Training on Synthetic Data Amplifies Bias.
• Fairness in Online Ad Delivery.
• Fairness without Sensitive Attributes via Knowledge Sharing.
• From the Fair Distribution of Predictions to the Fair Distribution of Social Goods: Evaluating the Impact of Fair Machine Learning on Long-Term Unemployment.
• Gender Bias Detection in Court Decisions: A Brazilian Case Study.
• Gender Representation Across Online Retail Products.
• Group Fairness via Group Consensus.
• Impact Charts: A Tool for Identifying Systematic Bias in Social Systems and Data.
• In the Walled Garden: Challenges and Opportunities for Research on the Practices of the AI Tech Industry.
• Insights From Insurance for Fair Machine Learning.
• Knowledge-Enhanced Language Models Are Not Bias-Proof: Situated Knowledge and Epistemic Injustice in AI.
• Large Language Models Portray Socially Subordinate Groups as More Homogeneous, Consistent with a Bias Observed in Humans.
• Law and the Emerging Political Economy of Algorithmic Audits.
• Lazy Data Practices Harm Fairness Research.
• Learning Fair Ranking Policies via Differentiable Optimization of Ordered Weighted Averages.
• Learning Fairness from Demonstrations via Inverse Reinforcement Learning.
• Misgendered During Moderation: How Transgender Bodies Make Visible Cisnormative Content Moderation Policies and Enforcement in a Meta Oversight Board Case.
• One Model Many Scores: Using Multiverse Analysis to Prevent Fairness Hacking and Evaluate the Influence of Model Design Decisions.
• Operationalizing the Search for Less Discriminatory Alternatives in Fair Lending.
• Overriding (in)justice: pretrial risk assessment administration on the frontlines.
• PreFAIR: Combining Partial Preferences for Fair Consensus Decision-making.
• Racial/Ethnic Categories in AI and Algorithmic Fairness: Why They Matter and What They Represent.
• Recommend Me? Designing Fairness Metrics with Providers.
• Regulating AI-Based Remote Biometric Identification. Investigating the Public Demand for Bans, Audits, and Public Database Registrations.
• Speaking of accent: A content analysis of accent misconceptions in ASR research.
• Tackling Language Modelling Bias in Support of Linguistic Diversity.
• The Conflict Between Algorithmic Fairness and Non-Discrimination: An Analysis of Fair Automated Hiring.
• The Dark Side of Dataset Scaling: Evaluating Racial Classification in Multimodal Models.
• The four-fifths rule is not disparate impact: A woeful tale of epistemic trespassing in algorithmic fairness.
• The Impact of Differential Feature Under-reporting on Algorithmic Fairness.
• The Impact of iBuying is About More Than Just Racial Disparities: Evidence from Mecklenburg County, NC.
• The Neutrality Fallacy: When Algorithmic Fairness Interventions are (Not) Positive Action.
• The unfair side of Privacy Enhancing Technologies: addressing the trade-offs between PETs and fairness.
• Trans-centered moderation: Trans technology creators and centering transness in platform and community governance: Trans-centered moderation.
• Unlawful Proxy Discrimination: A Framework for Challenging Inherently Discriminatory Algorithms.
• Using Property Elicitation to Understand the Impacts of Fairness Regularizers.

ICDM 2024

• CounterFair: Group Counterfactuals for Bias Detection, Mitigation and Subgroup Identification.
• DifFaiRec: Generative Fair Recommender with Conditional Diffusion Model.
• Reducing Unfairness in Distributed Community Detection.

ICLR 2024

• Achieving Fairness in Multi-Agent MDP Using Reinforcement Learning.
• Adversarial Attacks on Fairness of Graph Neural Networks.
• Aligning Relational Learning with Lipschitz Fairness.
• Are Models Biased on Text without Gender-related Language?
• Assessing Uncertainty in Similarity Scoring: Performance & Fairness in Face Recognition.
• Balancing Act: Constraining Disparate Impact in Sparse Models.
• BayesPrompt: Prompting Large-Scale Pre-Trained Language Models on Few-shot Inference via Debiased Domain Abstraction.
• Bias Runs Deep: Implicit Reasoning Biases in Persona-Assigned LLMs.
• Causal Fairness under Unobserved Confounding: A Neural Sensitivity Framework.
• DAFA: Distance-Aware Fair Adversarial Training.
• Debiasing Attention Mechanism in Transformer without Demographics.
• Deceptive Fairness Attacks on Graphs via Meta Learning.
• Demystifying Local & Global Fairness Trade-offs in Federated Learning Using Partial Information Decomposition.
• Empirical Likelihood for Fair Classification.
• Enhancing Group Fairness in Online Settings Using Oblique Decision Forests.
• f-FERM: A Scalable Framework for Robust Fair Empirical Risk Minimization.
• Fair and Efficient Contribution Valuation for Vertical Federated Learning.
• Fair Classifiers that Abstain without Harm.
• FairerCLIP: Debiasing CLIP’s Zero-Shot Predictions using Functions in RKHSs.
• fairret: a Framework for Differentiable Fairness Regularization Terms.
• FairSeg: A Large-Scale Medical Image Segmentation Dataset for Fairness Learning Using Segment Anything Model with Fair Error-Bound Scaling.
• FairTune: Optimizing Parameter Efficient Fine Tuning for Fairness in Medical Image Analysis.
• FFB: A Fair Fairness Benchmark for In-Processing Group Fairness Methods.
• Finetuning Text-to-Image Diffusion Models for Fairness.
• Interventional Fairness on Partially Known Causal Graphs: A Constrained Optimization Approach.
• Never Train from Scratch: Fair Comparison of Long-Sequence Models Requires Data-Driven Priors.
• On the Fairness ROAD: Robust Optimization for Adversarial Debiasing.
• Post-hoc bias scoring is optimal for fair classification.
• Prediction without Preclusion: Recourse Verification with Reachable Sets.
• Procedural Fairness Through Decoupling Objectionable Data Generating Components.
• Structural Fairness-aware Active Learning for Graph Neural Networks.
• TEDDY: Trimming Edges with Degree-based Discrimination Strategy.
• The Devil is in the Neurons: Interpreting and Mitigating Social Biases in Language Models.
• Time Fairness in Online Knapsack Problems.
• Towards Poisoning Fair Representations.
• Unprocessing Seven Years of Algorithmic Fairness.

ICML 2024

• Adapting Static Fairness to Sequential Decision-Making: Bias Mitigation Strategies towards Equal Long-term Benefit Rate.
• Counterfactual Metarules for Local and Global Recourse.
• Differentially Private Bias-Term Fine-tuning of Foundation Models.
• Differentially Private Post-Processing for Fair Regression.
• Disparate Impact on Group Accuracy of Linearization for Private Inference.
• Do Language Models Exhibit the Same Cognitive Biases in Problem Solving as Human Learners?
• Erasing the Bias: Fine-Tuning Foundation Models for Semi-Supervised Learning.
• Fair Classification with Partial Feedback: An Exploration-Based Data Collection Approach.
• Fair Federated Learning via the Proportional Veto Core.
• Fair Off-Policy Learning from Observational Data.
• Fair Risk Control: A Generalized Framework for Calibrating Multi-group Fairness Risks.
• FairProof : Confidential and Certifiable Fairness for Neural Networks.
• FRAPPÉ: A Group Fairness Framework for Post-Processing Everything.
• How Far Can Fairness Constraints Help Recover From Biased Data?
• Individual Fairness in Graph Decomposition.
• Intersectional Unfairness Discovery.
• Large Language Models are Geographically Biased.
• Learning Decision Trees and Forests with Algorithmic Recourse.
• LIDAO: Towards Limited Interventions for Debiasing (Large) Language Models.
• Monotone Individual Fairness.
• Networked Inequality: Preferential Attachment Bias in Graph Neural Network Link Prediction.
• On The Fairness Impacts of Hardware Selection in Machine Learning.
• On the Maximal Local Disparity of Fairness-Aware Classifiers.
• Remembering to Be Fair: Non-Markovian Fairness in Sequential Decision Making.
• Stability and Multigroup Fairness in Ranking with Uncertain Predictions.
• Standardized Interpretable Fairness Measures for Continuous Risk Scores.

IJCAI 2024

• A New Paradigm for Counterfactual Reasoning in Fairness and Recourse.
• Algorithmic Fairness in Distribution of Resources and Tasks.
• Automatic De-Biased Temporal-Relational Modeling for Stock Investment Recommendation.
• By Fair Means or Foul: Quantifying Collusion in a Market Simulation with Deep Reinforcement Learning.
• Cooperation and Fairness in Systems of Indirect Reciprocity.
• Design a Win-Win Strategy That Is Fair to Both Service Providers and Tasks When Rejection Is Not an Option.
• Down the Toxicity Rabbit Hole: A Framework to Bias Audit Large Language Models with Key Emphasis on Racism, Antisemitism, and Misogyny.
• Ensuring Fairness Stability for Disentangling Social Inequality in Access to Education: the FAiRDAS General Method.
• Fair Distribution of Delivery Orders.
• FairGT: A Fairness-aware Graph Transformer.
• FairReFuse: Referee-Guided Fusion for Multi-Modal Causal Fairness in Depression Detection.
• For the Misgendered Chinese in Gender Bias Research: Multi-Task Learning with Knowledge Distillation for Pinyin Name Gender Prediction.
• From Pink and Blue to a Rainbow Hue! Defying Gender Bias through Gender Neutralizing Text Transformations.
• Learning Fair Cooperation in Mixed-Motive Games with Indirect Reciprocity.
• Learning Fair Representations for Recommendation via Information Bottleneck Principle.
• LEEC for Judicial Fairness: A Legal Element Extraction Dataset with Extensive Extra-Legal Labels.
• Nonparametric Detection of Gerrymandering in Multiparty Plurality Elections.
• On the Effects of Fairness to Adversarial Vulnerability.
• Online Combinatorial Optimization with Group Fairness Constraints.
• Reassessing Evaluation Functions in Algorithmic Recourse: An Empirical Study from a Human-Centered Perspective.
• Supervised Algorithmic Fairness in Distribution Shifts: A Survey.
• The Impact of Features Used by Algorithms on Perceptions of Fairness.
• Towards Counterfactual Fairness-aware Domain Generalization in Changing Environments.
• Transforming Recommender Systems: Balancing Personalization, Fairness, and Human Values.
• Unmasking Societal Biases in Respiratory Support for ICU Patients through Social Determinants of Health.
• What Hides behind Unfairness? Exploring Dynamics Fairness in Reinforcement Learning.
• When Fairness Meets Privacy: Exploring Privacy Threats in Fair Binary Classifiers via Membership Inference Attacks.

KDD 2024

• Addressing Shortcomings in Fair Graph Learning Datasets: Towards a New Benchmark.
• AIM: Attributing, Interpreting, Mitigating Data Unfairness.
• Algorithmic Fairness Generalization under Covariate and Dependence Shifts Simultaneously.
• Bias and Unfairness in Information Retrieval Systems: New Challenges in the LLM Era.
• Counteracting Duration Bias in Video Recommendation via Counterfactual Watch Time.
• Debiased Recommendation with Noisy Feedback.
• Equity, Diversity & Inclusion (EDI): Special Day at ACM KDD 2024.
• Fair Column Subset Selection.
• FairMatch: Promoting Partial Label Learning by Unlabeled Samples.
• Fairness in Streaming Submodular Maximization Subject to a Knapsack Constraint.
• FUGNN: Harmonizing Fairness and Utility in Graph Neural Networks.
• Hate Speech Detection with Generalizable Target-aware Fairness.
• Mitigating Pooling Bias in E-commerce Search via False Negative Estimation.
• Neural Collapse Inspired Debiased Representation Learning for Min-max Fairness.
• Neural Retrievers are Biased Towards LLM-Generated Content.
• One Fits All: Learning Fair Graph Neural Networks for Various Sensitive Attributes.
• Path-Specific Causal Reasoning for Fairness-aware Cognitive Diagnosis.
• Performative Debias with Fair-exposure Optimization Driven by Strategic Agents in Recommender Systems.
• Popularity-Aware Alignment and Contrast for Mitigating Popularity Bias.
• Promoting Fairness and Priority in Selecting k-Winners Using IRV.
• Rethinking Fair Graph Neural Networks from Re-balancing.
• Sharing is Caring: A Practical Guide to FAIR(ER) Open Data Release.
• Toward Structure Fairness in Dynamic Graph Embedding: A Trend-aware Dual Debiasing Approach.
• Using Self-supervised Learning Can Improve Model Fairness.
• Your Neighbor Matters: Towards Fair Decisions Under Networked Interference.

NIPS 2024

• A Local Method for Satisfying Interventional Fairness with Partially Known Causal Graphs.
• A Taxonomy of Challenges to Curating Fair Datasets.
• A Unified Debiasing Approach for Vision-Language Models across Modalities and Tasks.
• ABCFair: an Adaptable Benchmark approach for Comparing Fairness Methods.
• Achievable Fairness on Your Data With Utility Guarantees.
• Are Your Models Still Fair? Fairness Attacks on Graph Neural Networks via Node Injections.
• Association of Objects May Engender Stereotypes: Mitigating Association-Engendered Stereotypes in Text-to-Image Generation.
• Bias Amplification in Language Model Evolution: An Iterated Learning Perspective.
• Bias and Volatility: A Statistical Framework for Evaluating Large Language Model’s Stereotypes and the Associated Generation Inconsistency.
• Conformal Classification with Equalized Coverage for Adaptively Selected Groups.
• Counterfactual Fairness by Combining Factual and Counterfactual Predictions.
• Cross-Care: Assessing the Healthcare Implications of Pre-training Data on Language Model Bias.
• Debiasing Synthetic Data Generated by Deep Generative Models.
• Do Counterfactually Fair Image Classifiers Satisfy Group Fairness? - A Theoretical and Empirical Study.
• Does Egalitarian Fairness Lead to Instability? The Fairness Bounds in Stable Federated Learning Under Altruistic Behaviors.
• Enhancing Robustness of Last Layer Two-Stage Fair Model Corrections.
• Fair Bilevel Neural Network (FairBiNN): On Balancing fairness and accuracy via Stackelberg Equilibrium.
• Fair Kernel K-Means: from Single Kernel to Multiple Kernel.
• Fair Wasserstein Coresets.
• FairJob: A Real-World Dataset for Fairness in Online Systems.
• FairMedFM: Fairness Benchmarking for Medical Imaging Foundation Models.
• Fairness in Social Influence Maximization via Optimal Transport.
• Fairness without Harm: An Influence-Guided Active Sampling Approach.
• Fairness-Aware Estimation of Graphical Models.
• Fairness-Aware Meta-Learning via Nash Bargaining.
• FairQueue: Rethinking Prompt Learning for Fair Text-to-Image Generation.
• FairWire: Fair Graph Generation.
• Improving Adversarial Robust Fairness via Anti-Bias Soft Label Distillation.
• Interpolating Item and User Fairness in Multi-Sided Recommendations.
• MAGNET: Improving the Multilingual Fairness of Language Models with Adaptive Gradient-Based Tokenization.
• Mind the Graph When Balancing Data for Fairness or Robustness.
• No-Regret Learning for Fair Multi-Agent Social Welfare Optimization.
• On Socially Fair Low-Rank Approximation and Column Subset Selection.
• OxonFair: A Flexible Toolkit for Algorithmic Fairness.
• Parameterized Approximation Schemes for Fair-Range Clustering.
• Probing Social Bias in Labor Market Text Generation by ChatGPT: A Masked Language Model Approach.
• Proportional Fairness in Non-Centroid Clustering.
• The Fairness-Quality Tradeoff in Clustering.
• The Fragility of Fairness: Causal Sensitivity Analysis for Fair Machine Learning.
• Theoretical and Empirical Insights into the Origins of Degree Bias in Graph Neural Networks.
• Towards Accurate and Fair Cognitive Diagnosis via Monotonic Data Augmentation.
• Towards Harmless Rawlsian Fairness Regardless of Demographic Prior.
• UniBias: Unveiling and Mitigating LLM Bias through Internal Attention and FFN Manipulation.
• User-item fairness tradeoffs in recommendations.
• Wasserstein Distributionally Robust Optimization through the Lens of Structural Causal Models and Individual Fairness.

SDM 2024

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UAI 2024

• BanditQ: Fair Bandits with Guaranteed Rewards.
• End-to-End Learning for Fair Multiobjective Optimization Under Uncertainty.
• Fair Active Learning in Low-Data Regimes.
• Group Fairness in Predict-Then-Optimize Settings for Restless Bandits.

WWW 2024

• Can One Embedding Fit All? A Multi-Interest Learning Paradigm Towards Improving User Interest Diversity Fairness.
• Causally Debiased Time-aware Recommendation.
• Debiasing Recommendation with Personal Popularity.
• Endowing Pre-trained Graph Models with Provable Fairness.
• Enhancing Fairness in Meta-learned User Modeling via Adaptive Sampling.
• Ensuring User-side Fairness in Dynamic Recommender Systems.
• Fair Graph Representation Learning via Sensitive Attribute Disentanglement.
• Fair Surveillance Assignment Problem.
• Fairness Rising from the Ranks: HITS and PageRank on Homophilic Networks.
• FairSync: Ensuring Amortized Group Exposure in Distributed Recommendation Retrieval.
• Fast and Accurate Fair k-Center Clustering in Doubling Metrics.
• Graph Fairness Learning under Distribution Shifts.
• Improving Item-side Fairness of Multimodal Recommendation via Modality Debiasing.
• Intersectional Two-sided Fairness in Recommendation.
• Item-side Fairness of Large Language Model-based Recommendation System.
• Privacy-Preserving and Fairness-Aware Federated Learning for Critical Infrastructure Protection and Resilience.
• Scalable and Provably Fair Exposure Control for Large-Scale Recommender Systems.

Others 2024

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WSDM 2024

• Debiasing Sequential Recommenders through Distributionally Robust Optimization over System Exposure.
• FairIF: Boosting Fairness in Deep Learning via Influence Functions with Validation Set Sensitive Attributes.
• Framework for Bias Detection in Machine Learning Models: A Fairness Approach.
• Interact with the Explanations: Causal Debiased Explainable Recommendation System.
• Pre-trained Recommender Systems: A Causal Debiasing Perspective.
• The Devil is in the Data: Learning Fair Graph Neural Networks via Partial Knowledge Distillation.

2023

AAAI 2023

• A Fair Generative Model Using LeCam Divergence.
• A Fair Incentive Scheme for Community Health Workers.
• Accurate Fairness: Improving Individual Fairness without Trading Accuracy.
• Advances in AI for Safety, Equity, and Well-Being on Web and Social Media: Detection, Robustness, Attribution, and Mitigation.
• An Efficient Algorithm for Fair Multi-Agent Multi-Armed Bandit with Low Regret.
• Censored Fairness through Awareness.
• CertiFair: A Framework for Certified Global Fairness of Neural Networks.
• Certifying Fairness of Probabilistic Circuits.
• Counterfactual Fairness Is Basically Demographic Parity.
• Debiased Fine-Tuning for Vision-Language Models by Prompt Regularization.
• Deep Learning on a Healthy Data Diet: Finding Important Examples for Fairness.
• DeepGemini: Verifying Dependency Fairness for Deep Neural Network.
• Equity Promotion in Public Transportation.
• Exploring Social Biases of Large Language Models in a College Artificial Intelligence Course.
• Fair Generative Models via Transfer Learning.
• Fair Representation Learning for Recommendation: A Mutual Information Perspective.
• Fair Short Paths in Vertex-Colored Graphs.
• Fair-CDA: Continuous and Directional Augmentation for Group Fairness.
• FairFed: Enabling Group Fairness in Federated Learning.
• Fairness and Explainability: Bridging the Gap towards Fair Model Explanations.
• Fairness and Welfare Quantification for Regret in Multi-Armed Bandits.
• Faster Fair Machine via Transferring Fairness Constraints to Virtual Samples.
• FedABC: Targeting Fair Competition in Personalized Federated Learning.
• FedMDFG: Federated Learning with Multi-Gradient Descent and Fair Guidance.
• For the Underrepresented in Gender Bias Research: Chinese Name Gender Prediction with Heterogeneous Graph Attention Network.
• How to Cut a Discrete Cake Fairly.
• Improvement-Focused Causal Recourse (ICR).
• Improving Fairness in Information Exposure by Adding Links.
• Interpreting Unfairness in Graph Neural Networks via Training Node Attribution.
• Minimax AUC Fairness: Efficient Algorithm with Provable Convergence.
• MixFairFace: Towards Ultimate Fairness via MixFair Adapter in Face Recognition.
• On Generalized Degree Fairness in Graph Neural Networks.
• Online Platforms and the Fair Exposure Problem under Homophily.
• People Taking Photos That Faces Never Share: Privacy Protection and Fairness Enhancement from Camera to User.
• Physics Guided Neural Networks for Time-Aware Fairness: An Application in Crop Yield Prediction.
• Popularizing Fairness: Group Fairness and Individual Welfare.
• Probably Approximate Shapley Fairness with Applications in Machine Learning.
• Quantify the Political Bias in News Edits: Experiments with Few-Shot Learners (Student Abstract).
• SimFair: A Unified Framework for Fairness-Aware Multi-Label Classification.
• Social Bias Meets Data Bias: The Impacts of Labeling and Measurement Errors on Fairness Criteria.
• Socially Optimal Non-discriminatory Restrictions for Continuous-Action Games.
• Sustaining Fairness via Incremental Learning.
• Towards Fair and Selectively Privacy-Preserving Models Using Negative Multi-Task Learning (Student Abstract).

AIES 2023

• “☑ Fairness Toolkits, A Checkbox Culture?” On the Factors that Fragment Developer Practices in Handling Algorithmic Harms.
• Adaptive Adversarial Training Does Not Increase Recourse Costs.
• ChatGPT Perpetuates Gender Bias in Machine Translation and Ignores Non-Gendered Pronouns: Findings across Bengali and Five other Low-Resource Languages.
• Evaluating Biased Attitude Associations of Language Models in an Intersectional Context.
• Evaluation of targeted dataset collection on racial equity in face recognition.
• Factoring the Matrix of Domination: A Critical Review and Reimagination of Intersectionality in AI Fairness.
• Fairness Implications of Encoding Protected Categorical Attributes.
• GATE: A Challenge Set for Gender-Ambiguous Translation Examples.
• Individual and Group-level considerations of Actionable Recourse.
• Iterative Partial Fulfillment of Counterfactual Explanations: Benefits and Risks.
• Learning from Discriminatory Training Data.
• Learning Optimal Fair Decision Trees: Trade-offs Between Interpretability, Fairness, and Accuracy.
• Mitigating Voter Attribute Bias for Fair Opinion Aggregation.
• Multicalibrated Regression for Downstream Fairness.
• Not So Fair: The Impact of Presumably Fair Machine Learning Models.
• Perceived Algorithmic Fairness using Organizational Justice Theory: An Empirical Case Study on Algorithmic Hiring.
• Sampling Individually-Fair Rankings that are Always Group Fair.
• Social Biases through the Text-to-Image Generation Lens.
• Stress-Testing Bias Mitigation Algorithms to Understand Fairness Vulnerabilities.
• Supporting Human-AI Collaboration in Auditing LLMs with LLMs.
• Target specification bias, counterfactual prediction, and algorithmic fairness in healthcare.
• Towards a Holistic Approach: Understanding Sociodemographic Biases in NLP Models using an Interdisciplinary Lens.
• Towards formalizing and assessing AI fairness.
• Towards User Guided Actionable Recourse.
• When Fair Classification Meets Noisy Protected Attributes.

AISTATS 2023

• A New Causal Decomposition Paradigm towards Health Equity.
• Doubly Fair Dynamic Pricing.
• Efficient fair PCA for fair representation learning.
• Fair learning with Wasserstein barycenters for non-decomposable performance measures.
• Fair Representation Learning with Unreliable Labels.
• FAIR: Fair Collaborative Active Learning with Individual Rationality for Scientific Discovery.
• Fast Feature Selection with Fairness Constraints.
• Feasible Recourse Plan via Diverse Interpolation.
• Improved Approximation for Fair Correlation Clustering.
• Mean Parity Fair Regression in RKHS.
• MMD-B-Fair: Learning Fair Representations with Statistical Testing.
• On the Privacy Risks of Algorithmic Recourse.
• Reinforcement Learning with Stepwise Fairness Constraints.
• Revisiting Fair-PAC Learning and the Axioms of Cardinal Welfare.
• Scalable Spectral Clustering with Group Fairness Constraints.
• Stochastic Methods for AUC Optimization subject to AUC-based Fairness Constraints.
• Toward Fairness in Text Generation via Mutual Information Minimization based on Importance Sampling.

BIGDATA 2023

• A Perspective on Data Categorization with Regard to Equity, Diversity, and Inclusion in Data Science.
• Aiming to Minimize Alcohol-Impaired Road Fatalities: Utilizing Fairness-Aware and Domain Knowledge-Infused Artificial Intelligence.
• Causal Fairness-Guided Dataset Reweighting using Neural Networks.
• CkanFAIR: a digital tool for assessing the FAIR principles.
• Developing a Large-Scale Language Model to Unveil and Alleviate Gender and Age Biases in Australian Job Ads.
• Quantitatively Evaluating the Validity of Contrastive Generators for Recourse.
• Randomized Response Has No Disparate Impact on Model Accuracy.
• Recommendation fairness and where to find it: An empirical study on fairness of user recommender systems.
• Striking a Balance in Fairness for Dynamic Systems Through Reinforcement Learning.

CIKM 2023

• ‘Choose your Data Wisely’: Active Learning based Selection with Multi-Objective Optimisation for Mitigating Stereotypes.
• Adaptation Speed Analysis for Fairness-aware Causal Models.
• Bias Invariant Approaches for Improving Word Embedding Fairness.
• CDR: Conservative Doubly Robust Learning for Debiased Recommendation.
• Counterfactual Graph Augmentation for Consumer Unfairness Mitigation in Recommender Systems.
• Fair&Share: Fast and Fair Multi-Criteria Selections.
• Fairness through Aleatoric Uncertainty.
• FARA: Future-aware Ranking Algorithm for Fairness Optimization.
• Intersectional Bias Mitigation in Pre-trained Language Models: A Quantum-Inspired Approach.
• PopDCL: Popularity-aware Debiased Contrastive Loss for Collaborative Filtering.
• Predictive Uncertainty-based Bias Mitigation in Ranking.
• RecRec: Algorithmic Recourse for Recommender Systems.
• RoCourseNet: Robust Training of a Prediction Aware Recourse Model.
• Test-Time Embedding Normalization for Popularity Bias Mitigation.
• Text Matching Improves Sequential Recommendation by Reducing Popularity Biases.
• Towards Fair Financial Services for All: A Temporal GNN Approach for Individual Fairness on Transaction Networks.
• Towards Fair Graph Neural Networks via Graph Counterfactual.

FAT* 2023

• “How Biased are Your Features?”: Computing Fairness Influence Functions with Global Sensitivity Analysis.
• “I wouldn’t say offensive but…”: Disability-Centered Perspectives on Large Language Models.
• “I’m fully who I am”: Towards Centering Transgender and Non-Binary Voices to Measure Biases in Open Language Generation.
• A Sociotechnical Audit: Assessing Police Use of Facial Recognition.
• ACROCPoLis: A Descriptive Framework for Making Sense of Fairness.
• Add-Remove-or-Relabel: Practitioner-Friendly Bias Mitigation via Influential Fairness.
• AI’s Regimes of Representation: A Community-centered Study of Text-to-Image Models in South Asia.
• Algorithmic Unfairness through the Lens of EU Non-Discrimination Law: Or Why the Law is not a Decision Tree.
• Algorithms as Social-Ecological-Technological Systems: an Environmental Justice Lens on Algorithmic Audits.
• An Empirical Analysis of Racial Categories in the Algorithmic Fairness Literature.
• Bias Against 93 Stigmatized Groups in Masked Language Models and Downstream Sentiment Classification Tasks.
• Bias on Demand: A Modelling Framework That Generates Synthetic Data With Bias.
• Datafication Genealogies beyond Algorithmic Fairness: Making Up Racialised Subjects.
• Detecting disparities in police deployments using dashcam data.
• Discrimination through Image Selection by Job Advertisers on Facebook.
• Disentangling and Operationalizing AI Fairness at LinkedIn.
• Diverse Perspectives Can Mitigate Political Bias in Crowdsourced Content Moderation.
• Domain Adaptive Decision Trees: Implications for Accuracy and Fairness.
• Easily Accessible Text-to-Image Generation Amplifies Demographic Stereotypes at Large Scale.
• Enhancing AI fairness through impact assessment in the European Union: a legal and computer science perspective.
• Examining risks of racial biases in NLP tools for child protective services.
• FairAssign: Stochastically Fair Driver Assignment in Gig Delivery Platforms.
• Fairer Together: Mitigating Disparate Exposure in Kemeny Rank Aggregation.
• Fairness Auditing in Urban Decisions using LP-based Data Combination.
• Fairness in machine learning from the perspective of sociology of statistics: How machine learning is becoming scientific by turning its back on metrological realism.
• Gender Animus Can Still Exist Under Favorable Disparate Impact: a Cautionary Tale from Online P2P Lending.
• Group fairness without demographics using social networks.
• Group-Fair Classification with Strategic Agents.
• Help or Hinder? Evaluating the Impact of Fairness Metrics and Algorithms in Visualizations for Consensus Ranking.
• How Redundant are Redundant Encodings? Blindness in the Wild and Racial Disparity when Race is Unobserved.
• Improving Fairness in AI Models on Electronic Health Records: The Case for Federated Learning Methods.
• In her Shoes: Gendered Labelling in Crowdsourced Safety Perceptions Data from India.
• In the Name of Fairness: Assessing the Bias in Clinical Record De-identification.
• Investigating Practices and Opportunities for Cross-functional Collaboration around AI Fairness in Industry Practice.
• Maximal fairness.
• Measuring and mitigating voting access disparities: a study of race and polling locations in Florida and North Carolina.
• Multi-dimensional Discrimination in Law and Machine Learning - A Comparative Overview.
• Multi-Target Multiplicity: Flexibility and Fairness in Target Specification under Resource Constraints.
• Navigating the Audit Landscape: A Framework for Developing Transparent and Auditable XR.
• On (assessing) the fairness of risk score models.
• On The Impact of Machine Learning Randomness on Group Fairness.
• On the Independence of Association Bias and Empirical Fairness in Language Models.
• Personalized Pricing with Group Fairness Constraint.
• Preventing Discriminatory Decision-making in Evolving Data Streams.
• Representation in AI Evaluations.
• Representation Online Matters: Practical End-to-End Diversification in Search and Recommender Systems.
• Representation, Self-Determination, and Refusal: Queer People’s Experiences with Targeted Advertising.
• Robustness Implies Fairness in Causal Algorithmic Recourse.
• Runtime Monitoring of Dynamic Fairness Properties.
• Simplicity Bias Leads to Amplified Performance Disparities.
• Skin Deep: Investigating Subjectivity in Skin Tone Annotations for Computer Vision Benchmark Datasets.
• The Many Faces of Fairness: Exploring the Institutional Logics of Multistakeholder Microlending Recommendation.
• The Misuse of AUC: What High Impact Risk Assessment Gets Wrong.
• The Possibility of Fairness: Revisiting the Impossibility Theorem in Practice.
• The Privacy-Bias Tradeoff: Data Minimization and Racial Disparity Assessments in U.S. Government.
• The Progression of Disparities within the Criminal Justice System: Differential Enforcement and Risk Assessment Instruments.
• The Slow Violence of Surveillance Capitalism: How Online Behavioral Advertising Harms People.
• UNFair: Search Engine Manipulation, Undetectable by Amortized Inequity.
• Using Supervised Learning to Estimate Inequality in the Size and Persistence of Income Shocks.
• Which Stereotypes Are Moderated and Under-Moderated in Search Engine Autocompletion?
• You Sound Depressed: A Case Study on Sonde Health’s Diagnostic Use of Voice Analysis AI.
• Your Browsing History May Cost You: A Framework for Discovering Differential Pricing in Non-Transparent Markets.

ICDM 2023

• A Counterfactual Fair Model for Longitudinal Electronic Health Records via Deconfounder.
• Equipping Federated Graph Neural Networks with Structure-aware Group Fairness.
• Graph Sampling based Fairness-aware Recommendation over Sensitive Attribute Removal.
• Mitigating Multisource Biases in Graph Neural Networks via Real Counterfactual Samples.
• Unfairness in Distributed Graph Frameworks.

ICLR 2023

• Calibration Matters: Tackling Maximization Bias in Large-scale Advertising Recommendation Systems.
• Confidential-PROFITT: Confidential PROof of FaIr Training of Trees.
• Contextual bandits with concave rewards, and an application to fair ranking.
• Disparate Impact in Differential Privacy from Gradient Misalignment.
• Distributionally Robust Recourse Action.
• Equal Improvability: A New Fairness Notion Considering the Long-term Impact.
• Fair Attribute Completion on Graph with Missing Attributes.
• FaiREE: fair classification with finite-sample and distribution-free guarantee.
• FairGBM: Gradient Boosting with Fairness Constraints.
• Fairness and Accuracy under Domain Generalization.
• Fairness-aware Contrastive Learning with Partially Annotated Sensitive Attributes.
• FIFA: Making Fairness More Generalizable in Classifiers Trained on Imbalanced Data.
• Human-Guided Fair Classification for Natural Language Processing.
• Learning Fair Graph Representations via Automated Data Augmentations.
• MEDFAIR: Benchmarking Fairness for Medical Imaging.
• Probabilistically Robust Recourse: Navigating the Trade-offs between Costs and Robustness in Algorithmic Recourse.
• Re-weighting Based Group Fairness Regularization via Classwise Robust Optimization.
• Robust Fair Clustering: A Novel Fairness Attack and Defense Framework.
• Stochastic Differentially Private and Fair Learning.
• TDR-CL: Targeted Doubly Robust Collaborative Learning for Debiased Recommendations.
• Tier Balancing: Towards Dynamic Fairness over Underlying Causal Factors.
• UniMax: Fairer and More Effective Language Sampling for Large-Scale Multilingual Pretraining.

ICML 2023

• Approximation Algorithms for Fair Range Clustering.
• Collaborative Causal Inference with Fair Incentives.
• Differential Privacy has Bounded Impact on Fairness in Classification.
• Differential Privacy, Linguistic Fairness, and Training Data Influence: Impossibility and Possibility Theorems for Multilingual Language Models.
• Fair and Accurate Decision Making through Group-Aware Learning.
• Fair and Optimal Classification via Post-Processing.
• Fair and Robust Estimation of Heterogeneous Treatment Effects for Policy Learning.
• Fair Densities via Boosting the Sufficient Statistics of Exponential Families.
• Fair Neighbor Embedding.
• Fair yet Asymptotically Equal Collaborative Learning.
• FAIRER: Fairness as Decision Rationale Alignment.
• Fairness in Streaming Submodular Maximization over a Matroid Constraint.
• FARE: Provably Fair Representation Learning with Practical Certificates.
• Generalized Disparate Impact for Configurable Fairness Solutions in ML.
• Generalized Reductions: Making any Hierarchical Clustering Fair and Balanced with Low Cost.
• Improving Fair Training under Correlation Shifts.
• Individually Fair Learning with One-Sided Feedback.
• Learning Antidote Data to Individual Unfairness.
• Loss Balancing for Fair Supervised Learning.
• Matrix Estimation for Individual Fairness.
• On the Impact of Algorithmic Recourse on Social Segregation.
• On the Within-Group Fairness of Screening Classifiers.
• Same Pre-training Loss, Better Downstream: Implicit Bias Matters for Language Models.
• Subset Selection Based On Multiple Rankings in the Presence of Bias: Effectiveness of Fairness Constraints for Multiwinner Voting Score Functions.
• Superhuman Fairness.
• Weak Proxies are Sufficient and Preferable for Fairness with Missing Sensitive Attributes.
• When do Minimax-fair Learning and Empirical Risk Minimization Coincide?

IJCAI 2023

• A Survey on Intersectional Fairness in Machine Learning: Notions, Mitigation, and Challenges.
• Assessing and Enforcing Fairness in the AI Lifecycle.
• Bias On Demand: Investigating Bias with a Synthetic Data Generator.
• CGS: Coupled Growth and Survival Model with Cohort Fairness.
• Decoupling with Entropy-based Equalization for Semi-Supervised Semantic Segmentation.
• Disentangling Societal Inequality from Model Biases: Gender Inequality in Divorce Court Proceedings.
• Fairness and Representation in Satellite-Based Poverty Maps: Evidence of Urban-Rural Disparities and Their Impacts on Downstream Policy.
• Fairness and Stability in Complex Domains.
• Fast and Differentially Private Fair Clustering.
• FEAMOE: Fair, Explainable and Adaptive Mixture of Experts.
• First-Choice Maximality Meets Ex-ante and Ex-post Fairness.
• Group Fairness in Set Packing Problems.
• Incentivizing Recourse through Auditing in Strategic Classification.
• Interpretability and Fairness in Machine Learning: A Formal Methods Approach.
• Learning to Design Fair and Private Voting Rules (Extended Abstract).
• On Adversarial Robustness of Demographic Fairness in Face Attribute Recognition.
• On the Fairness Impacts of Private Ensembles Models.
• Online Certification of Preference-Based Fairness for Personalized Recommender Systems (Extended Abstract).
• Promoting Gender Equality through Gender-biased Language Analysis in Social Media.
• Pushing the Limits of Fairness in Algorithmic Decision-Making.
• Sampling Ex-Post Group-Fair Rankings.
• Self-Recover: Forecasting Block Maxima in Time Series from Predictors with Disparate Temporal Coverage Using Self-Supervised Learning.
• SF-PATE: Scalable, Fair, and Private Aggregation of Teacher Ensembles.
• The Effects of AI Biases and Explanations on Human Decision Fairness: A Case Study of Bidding in Rental Housing Markets.
• Towards Gender Fairness for Mental Health Prediction.

KDD 2023

• A Personalized Automated Bidding Framework for Fairness-aware Online Advertising.
• Addressing Bias and Fairness in Machine Learning: A Practical Guide and Hands-on Tutorial.
• Anomaly Detection with Score Distribution Discrimination.
• Counterfactual Video Recommendation for Duration Debiasing.
• Debiasing Recommendation by Learning Identifiable Latent Confounders.
• Fair Multilingual Vandalism Detection System for Wikipedia.
• FairCod: A Fairness-aware Concurrent Dispatch System for Large-scale Instant Delivery Services.
• Fairness in Graph Machine Learning: Recent Advances and Future Prospectives.
• Fairness-Aware Continuous Predictions of Multiple Analytics Targets in Dynamic Networks.
• Grace: Graph Self-Distillation and Completion to Mitigate Degree-Related Biases.
• Influence Maximization with Fairness at Scale.
• Learning for Counterfactual Fairness from Observational Data.
• Minimizing Hitting Time between Disparate Groups with Shortcut Edges.
• Online Fairness Auditing through Iterative Refinement.
• Path-Specific Counterfactual Fairness for Recommender Systems.
• Privacy Matters: Vertical Federated Linear Contextual Bandits for Privacy Protected Recommendation.
• Querywise Fair Learning to Rank through Multi-Objective Optimization.
• SURE: Robust, Explainable, and Fair Classification without Sensitive Attributes.
• Towards Fair Disentangled Online Learning for Changing Environments.
• Towards Fairness in Personalized Ads Using Impression Variance Aware Reinforcement Learning.

NIPS 2023

• Adapting Fairness Interventions to Missing Values.
• Aleatoric and Epistemic Discrimination: Fundamental Limits of Fairness Interventions.
• Auditing Fairness by Betting.
• Building Socio-culturally Inclusive Stereotype Resources with Community Engagement.
• Causal Context Connects Counterfactual Fairness to Robust Prediction and Group Fairness.
• Causal Fairness for Outcome Control.
• Certification of Distributional Individual Fairness.
• Chasing Fairness Under Distribution Shift: A Model Weight Perturbation Approach.
• Consensus and Subjectivity of Skin Tone Annotation for ML Fairness.
• Core-sets for Fair and Diverse Data Summarization.
• Counterfactually Fair Representation.
• DIFFER: Decomposing Individual Reward for Fair Experience Replay in Multi-Agent Reinforcement Learning.
• Doubly Constrained Fair Clustering.
• Equal Opportunity of Coverage in Fair Regression.
• Estimating and Controlling for Equalized Odds via Sensitive Attribute Predictors.
• Fair Adaptive Experiments.
• Fair Canonical Correlation Analysis.
• Fair Graph Distillation.
• Fair Streaming Principal Component Analysis: Statistical and Algorithmic Viewpoint.
• Fair, Polylog-Approximate Low-Cost Hierarchical Clustering.
• FairLISA: Fair User Modeling with Limited Sensitive Attributes Information.
• Fairly Recommending with Social Attributes: A Flexible and Controllable Optimization Approach.
• Fairness Aware Counterfactuals for Subgroups.
• Fairness Continual Learning Approach to Semantic Scene Understanding in Open-World Environments.
• Fairness-guided Few-shot Prompting for Large Language Models.
• Group Fairness in Peer Review.
• H-nobs: Achieving Certified Fairness and Robustness in Distributed Learning on Heterogeneous Datasets.
• Individual Arbitrariness and Group Fairness.
• Language Model Tokenizers Introduce Unfairness Between Languages.
• Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias.
• Long-Term Fairness with Unknown Dynamics.
• On Measuring Fairness in Generative Models.
• Optimal and Fair Encouragement Policy Evaluation and Learning.
• Rethinking Bias Mitigation: Fairer Architectures Make for Fairer Face Recognition.
• Revisiting Adversarial Robustness Distillation from the Perspective of Robust Fairness.
• Scalable Fair Influence Maximization.
• Small Total-Cost Constraints in Contextual Bandits with Knapsacks, with Application to Fairness.
• Spuriosity Rankings: Sorting Data to Measure and Mitigate Biases.
• Uncovering and Quantifying Social Biases in Code Generation.
• Variational Imbalanced Regression: Fair Uncertainty Quantification via Probabilistic Smoothing.
• VisoGender: A dataset for benchmarking gender bias in image-text pronoun resolution.

SDM 2023

• Fairness-aware Multi-view Clustering.
• Group AdaBoost with Fairness Constraint.
• Max-Min Diversification with Fairness Constraints: Exact and Approximation Algorithms.
• Missed Opportunities in Fair AI.
• On Improving Fairness of AI Models with Synthetic Minority Oversampling Techniques.
• RELIANT: Fair Knowledge Distillation for Graph Neural Networks.

UAI 2023

• Fairness-aware class imbalanced learning on multiple subgroups.

WWW 2023

• A Method to Assess and Explain Disparate Impact in Online Retailing.
• Controllable Universal Fair Representation Learning.
• Debiased Contrastive Learning for Sequential Recommendation.
• DualFair: Fair Representation Learning at Both Group and Individual Levels via Contrastive Self-supervision.
• Fair Graph Representation Learning via Diverse Mixture-of-Experts.
• Fairly Adaptive Negative Sampling for Recommendations.
• Fairness in model-sharing games.
• Fairness-Aware Clique-Preserving Spectral Clustering of Temporal Graphs.
• Improving Recommendation Fairness via Data Augmentation.
• Maximizing Submodular Functions for Recommendation in the Presence of Biases.
• P-MMF: Provider Max-min Fairness Re-ranking in Recommender System.
• Path-specific Causal Fair Prediction via Auxiliary Graph Structure Learning.
• Scoping Fairness Objectives and Identifying Fairness Metrics for Recommender Systems: The Practitioners’ Perspective.
• Time-manipulation Attack: Breaking Fairness against Proof of Authority Aura.

Others 2023

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WSDM 2023

• BLADE: Biased Neighborhood Sampling based Graph Neural Network for Directed Graphs.
• Incorporating Fairness in Large Scale NLU Systems.
• Marginal-Certainty-Aware Fair Ranking Algorithm.
• Never Too Late to Learn: Regularizing Gender Bias in Coreference Resolution.
• Pairwise Fairness in Ranking as a Dissatisfaction Measure.
• Reducing Negative Effects of the Biases of Language Models in Zero-Shot Setting.
• Uncertainty Quantification for Fairness in Two-Stage Recommender Systems.

2022

AAAI 2022

• Locally Fair Partitioning.
• Truthful and Fair Mechanisms for Matroid-Rank Valuations.
• A Little Charity Guarantees Fair Connected Graph Partitioning.
• On Testing for Discrimination Using Causal Models.
• Online Certification of Preference-Based Fairness for Personalized Recommender Systems.
• Modification-Fair Cluster Editing.
• Achieving Counterfactual Fairness for Causal Bandit.
• Group-Aware Threshold Adaptation for Fair Classification.
• Fast and Efficient MMD-Based Fair PCA via Optimization over Stiefel Manifold.
• On the Impossibility of Non-trivial Accuracy in Presence of Fairness Constraints.
• Cooperative Multi-Agent Fairness and Equivariant Policies.
• Why Fair Labels Can Yield Unfair Predictions: Graphical Conditions for Introduced Unfairness.
• Algorithmic Fairness Verification with Graphical Models.
• Achieving Long-Term Fairness in Sequential Decision Making.
• Fairness without Imputation: A Decision Tree Approach for Fair Prediction with Missing Values.
• On the Fairness of Causal Algorithmic Recourse.
• Attention Biasing and Context Augmentation for Zero-Shot Control of Encoder-Decoder Transformers for Natural Language Generation.
• Socially Fair Mitigation of Misinformation on Social Networks via Constraint Stochastic Optimization.
• Interpreting Gender Bias in Neural Machine Translation: Multilingual Architecture Matters.
• Word Embeddings via Causal Inference: Gender Bias Reducing and Semantic Information Preserving.
• Has CEO Gender Bias Really Been Fixed? Adversarial Attacking and Improving Gender Fairness in Image Search.
• FairFoody: Bringing In Fairness in Food Delivery.
• Gradual (In)Compatibility of Fairness Criteria.
• Unmasking the Mask - Evaluating Social Biases in Masked Language Models.
• CrossWalk: Fairness-Enhanced Node Representation Learning.
• Fair Conformal Predictors for Applications in Medical Imaging.
• Investigations of Performance and Bias in Human-AI Teamwork in Hiring.
• Fairness by “Where”: A Statistically-Robust and Model-Agnostic Bi-level Learning Framework.
• Longitudinal Fairness with Censorship.
• Anatomizing Bias in Facial Analysis.
• Combating Sampling Bias: A Self-Training Method in Credit Risk Models.
• Reproducibility as a Mechanism for Teaching Fairness, Accountability, Confidentiality, and Transparency in Artificial Intelligence.
• LITMUS Predictor: An AI Assistant for Building Reliable, High-Performing and Fair Multilingual NLP Systems.

AIES 2022

• The Limits of Fairness.
• Beyond Fairness and Explanation: Foundations of Trustworthiness of Artificial Agents.
• Long-term Dynamics of Fairness Intervention in Connection Recommender Systems.
• SCALES: From Fairness Principles to Constrained Decision-Making.
• Fairness in Agreement With European Values: An Interdisciplinary Perspective on AI Regulation.
• FINS Auditing Framework: Group Fairness for Subset Selections.
• Gender Bias in Word Embeddings: A Comprehensive Analysis of Frequency, Syntax, and Semantics.
• Fairness via Explanation Quality: Evaluating Disparities in the Quality of Post hoc Explanations.
• Does AI De-Bias Recruitment?: Race, Gender, and AI’s ‘Eradication of Differences Between Groups’.
• An Ontology for Fairness Metrics.
• Understanding Decision Subjects’ Fairness Perceptions and Retention in Repeated Interactions with AI-Based Decision Systems.
• FairCanary: Rapid Continuous Explainable Fairness.
• Learning Fairer Interventions.
• Algorithmic Fairness and Structural Injustice: Insights from Feminist Political Philosophy.
• Equalizing Credit Opportunity in Algorithms: Aligning Algorithmic Fairness Research with U.S. Fair Lending Regulation.
• Data-Centric Factors in Algorithmic Fairness.
• Contrastive Counterfactual Fairness in Algorithmic Decision-Making.
• Measuring Gender Bias in Word Embeddings of Gendered Languages Requires Disentangling Grammatical Gender Signals.
• A Dynamic Decision-Making Framework Promoting Long-Term Fairness.
• From Coded Bias to Existential Threat: Expert Frames and the Epistemic Politics of AI Governance.
• Strategic Best Response Fairness in Fair Machine Learning.
• Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation.
• Socially-Aware Artificial Intelligence for Fair Mobility.
• Bias in Hate Speech and Toxicity Detection.
• What’s (Not) Ideal about Fair Machine Learning?
• Fair, Robust, and Data-Efficient Machine Learning in Healthcare.

CIKM 2022

• RAGUEL: Recourse-Aware Group Unfairness Elimination.
• Quantifying and Mitigating Popularity Bias in Conversational Recommender Systems.
• Debiased Balanced Interleaving at Amazon Search.
• Cascaded Debiasing: Studying the Cumulative Effect of Multiple Fairness-Enhancing Interventions.
• Towards Fairer Classifier via True Fairness Score Path.
• Incorporating Fairness in Large-scale Evacuation Planning.
• Causal Intervention for Sentiment De-biasing in Recommendation.
• Debiasing Neighbor Aggregation for Graph Neural Network in Recommender Systems.
• Do Graph Neural Networks Build Fair User Models? Assessing Disparate Impact and Mistreatment in Behavioural User Profiling.
• Balancing Utility and Exposure Fairness for Integrated Ranking with Reinforcement Learning.
• How Does the Crowd Impact the Model? A Tool for Raising Awareness of Social Bias in Crowdsourced Training Data.

FAT* 2022

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ICLR 2022

• Is Fairness Only Metric Deep? Evaluating and Addressing Subgroup Gaps in Deep Metric Learning.
• Fair Normalizing Flows.
• Distributionally Robust Fair Principal Components via Geodesic Descents.
• FairCal: Fairness Calibration for Face Verification.
• Fairness Guarantees under Demographic Shift.
• Generalized Demographic Parity for Group Fairness.
• Fairness in Representation for Multilingual NLP: Insights from Controlled Experiments on Conditional Language Modeling.

ICDM 2022

• Adversarial Inter-Group Link Injection Degrades the Fairness of Graph Neural Networks.
• Empirical analysis of fairness-aware data segmentation.
• Equal Confusion Fairness: Measuring Group-Based Disparities in Automated Decision Systems.
• Fairness-Aware Graph Sampling for Network Analysis.
• Learning About People’s Attitude Towards Food Available in India and Its Implications for Fair AI-based Systems.
• Mitigating Popularity Bias in Recommendation with Unbalanced Interactions: A Gradient Perspective.
• Toward Unified Data and Algorithm Fairness via Adversarial Data Augmentation and Adaptive Model Fine-tuning.
• Towards Fair Representation Learning in Knowledge Graph with Stable Adversarial Debiasing.
• Unfair AI: It Isn’t Just Biased Data.

ICML 2022

• Active Sampling for Min-Max Fairness.
• Fair and Fast k-Center Clustering for Data Summarization.
• Fairness with Adaptive Weights.
• Mitigating Gender Bias in Face Recognition using the von Mises-Fisher Mixture Model.
• Fair Generalized Linear Models with a Convex Penalty.
• Contrastive Mixture of Posteriors for Counterfactual Inference, Data Integration and Fairness.
• Input-agnostic Certified Group Fairness via Gaussian Parameter Smoothing.
• Learning fair representation with a parametric integral probability metric.
• Achieving Fairness at No Utility Cost via Data Reweighing with Influence.
• Rethinking Fano’s Inequality in Ensemble Learning.
• Causal Conceptions of Fairness and their Consequences.
• A Convergence Theory for SVGD in the Population Limit under Talagrand’s Inequality T1.
• Selective Regression under Fairness Criteria.
• Metric-Fair Active Learning.
• Fair Representation Learning through Implicit Path Alignment.

IJCAI 2022

• Individual Fairness Guarantees for Neural Networks.
• SoFaiR: Single Shot Fair Representation Learning.
• Fairness without the Sensitive Attribute via Causal Variational Autoencoder.
• Counterfactual Interpolation Augmentation (CIA): A Unified Approach to Enhance Fairness and Explainability of DNN.
• Post-processing of Differentially Private Data: A Fairness Perspective.
• Differential Privacy and Fairness in Decisions and Learning Tasks: A Survey.
• Extending Decision Tree to Handle Multiple Fairness Criteria.

KDD 2022

• Debiasing the Cloze Task in Sequential Recommendation with Bidirectional Transformers.
• On Structural Explanation of Bias in Graph Neural Networks.
• Fair Labeled Clustering.
• Fair Representation Learning: An Alternative to Mutual Information.
• UD-GNN: Uncertainty-aware Debiased Training on Semi-Homophilous Graphs.
• Learning Fair Representation via Distributional Contrastive Disentanglement.
• Fair and Interpretable Models for Survival Analysis.
• GUIDE: Group Equality Informed Individual Fairness in Graph Neural Networks.
• Clustering with Fair-Center Representation: Parameterized Approximation Algorithms and Heuristics.
• Make Fairness More Fair: Fair Item Utility Estimation and Exposure Re-Distribution.
• Partial Label Learning with Discrimination Augmentation.
• Improving Fairness in Graph Neural Networks via Mitigating Sensitive Attribute Leakage.
• Debiasing Learning for Membership Inference Attacks Against Recommender Systems.
• Invariant Preference Learning for General Debiasing in Recommendation.
• Comprehensive Fair Meta-learned Recommender System.
• Counteracting User Attention Bias in Music Streaming Recommendation via Reward Modification.
• Adaptive Fairness-Aware Online Meta-Learning for Changing Environments.
• Optimizing Long-Term Efficiency and Fairness in Ride-Hailing via Joint Order Dispatching and Driver Repositioning.
• Deconfounding Duration Bias in Watch-time Prediction for Video Recommendation.
• The Battlefront of Combating Misinformation and Coping with Media Bias.
• Algorithmic Fairness on Graphs: Methods and Trends.
• Temporal Graph Learning for Financial World: Algorithms, Scalability, Explainability & Fairness.

NIPS 2022

• Counterfactual Fairness with Partially Known Causal Graph.
• Combinatorial Bandits with Linear Constraints: Beyond Knapsacks and Fairness.
• Fairness in Federated Learning via Core-Stability.
• FairVFL: A Fair Vertical Federated Learning Framework with Contrastive Adversarial Learning.
• Policy Optimization with Advantage Regularization for Long-Term Fairness in Decision Systems.
• Fairness Transferability Subject to Bounded Distribution Shift.
• Conformalized Fairness via Quantile Regression.
• Are Two Heads the Same as One? Identifying Disparate Treatment in Fair Neural Networks.
• Bounding and Approximating Intersectional Fairness through Marginal Fairness.
• All Politics is Local: Redistricting via Local Fairness.
• The price of unfairness in linear bandits with biased feedback.
• Fairness without Demographics through Knowledge Distillation.
• Diagnosing failures of fairness transfer across distribution shift in real-world medical settings.
• Fair Wrapping for Black-box Predictions.
• Fair Rank Aggregation.
• Group Meritocratic Fairness in Linear Contextual Bandits.
• Debiasing Graph Neural Networks via Learning Disentangled Causal Substructure.
• On the Tradeoff Between Robustness and Fairness.
• Self-Supervised Fair Representation Learning without Demographics.
• Fair Bayes-Optimal Classifiers Under Predictive Parity.
• Debiased, Longitudinal and Coordinated Drug Recommendation through Multi-Visit Clinic Records.
• DeepMed: Semiparametric Causal Mediation Analysis with Debiased Deep Learning.
• Domain Adaptation meets Individual Fairness. And they get along.
• Personalized Federated Learning towards Communication Efficiency, Robustness and Fairness.
• Certifying Some Distributional Fairness with Subpopulation Decomposition.
• Fair Ranking with Noisy Protected Attributes.
• Uncovering the Structural Fairness in Graph Contrastive Learning.
• Transferring Fairness under Distribution Shifts via Fair Consistency Regularization.
• Pushing the limits of fairness impossibility: Who’s the fairest of them all?
• Optimal Transport of Classifiers to Fairness.
• On Learning Fairness and Accuracy on Multiple Subgroups.
• Fairness Reprogramming.
• Beyond Adult and COMPAS: Fair Multi-Class Prediction via Information Projection.
• Fair and Optimal Decision Trees: A Dynamic Programming Approach.

SDM 2022

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UAI 2022

• Active approximately metric-fair learning.
• Quadratic metric elicitation for fairness and beyond.
• How unfair is private learning?

WWW 2022

• FairGAN: GANs-based Fairness-aware Learning for Recommendations with Implicit Feedback.
• EDITS: Modeling and Mitigating Data Bias for Graph Neural Networks.
• Fair k-Center Clustering in MapReduce and Streaming Settings.
• CBR: Context Bias aware Recommendation for Debiasing User Modeling and Click Prediction✱.
• Left or Right: A Peek into the Political Biases in Email Spam Filtering Algorithms During US Election 2020.
• Controlled Analyses of Social Biases in Wikipedia Bios.
• What Does Perception Bias on Social Networks Tell Us About Friend Count Satisfaction?
• Fairness Audit of Machine Learning Models with Confidential Computing.
• End-to-End Learning for Fair Ranking Systems.
• Link Recommendations for PageRank Fairness.
• Privacy-Preserving Fair Learning of Support Vector Machine with Homomorphic Encryption.
• Alexa, in you, I trust! Fairness and Interpretability Issues in E-commerce Search through Smart Speakers.
• Regulatory Instruments for Fair Personalized Pricing.

Others 2022

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WSDM 2022

• k-Clustering with Fair Outliers.
• Toward Pareto Efficient Fairness-Utility Trade-off in Recommendation through Reinforcement Learning.
• Introducing the Expohedron for Efficient Pareto-optimal Fairness-Utility Amortizations in Repeated Rankings.
• Diversified Subgraph Query Generation with Group Fairness.
• Learning Fair Node Representations with Graph Counterfactual Fairness.
• Understanding and Mitigating the Effect of Outliers in Fair Ranking.
• Enumerating Fair Packages for Group Recommendations.
• Towards Fair Classifiers Without Sensitive Attributes: Exploring Biases in Related Features.
• Fighting Mainstream Bias in Recommender Systems via Local Fine Tuning.

2021

AAAI 2021

• Learning Disentangled Representation for Fair Facial Attribute Classification via Fairness-aware Information Alignment.
• Fairness-aware News Recommendation with Decomposed Adversarial Learning.
• Fair and Truthful Mechanisms for Dichotomous Valuations.
• Protecting the Protected Group: Circumventing Harmful Fairness.
• Fairness, Semi-Supervised Learning, and More: A General Framework for Clustering with Stochastic Pairwise Constraints.
• The Importance of Modeling Data Missingness in Algorithmic Fairness: A Causal Perspective.
• Controllable Guarantees for Fair Outcomes via Contrastive Information Estimation.
• Constructing a Fair Classifier with Generated Fair Data.
• Improving Fairness and Privacy in Selection Problems.
• Counterfactual Fairness with Disentangled Causal Effect Variational Autoencoder.
• Exacerbating Algorithmic Bias through Fairness Attacks.
• Minimum Robust Multi-Submodular Cover for Fairness.
• Robust Fairness Under Covariate Shift.
• Differentially Private and Fair Deep Learning: A Lagrangian Dual Approach.
• Fairness in Forecasting and Learning Linear Dynamical Systems.
• Variational Fair Clustering.
• Individual Fairness in Kidney Exchange Programs.
• Fair Representations by Compression.
• Fair Influence Maximization: a Welfare Optimization Approach.
• Group Fairness by Probabilistic Modeling with Latent Fair Decisions.
• How Linguistically Fair Are Multilingual Pre-Trained Language Models?
• Fairness in Influence Maximization through Randomization.
• Fair and Interpretable Algorithmic Hiring using Evolutionary Many Objective Optimization.

AISTATS 2021

• Learning Individually Fair Classifier with Path-Specific Causal-Effect Constraint.
• Learning Smooth and Fair Representations.
• Learning Fair Scoring Functions: Bipartite Ranking under ROC-based Fairness Constraints.
• Algorithms for Fairness in Sequential Decision Making.
• All of the Fairness for Edge Prediction with Optimal Transport.
• Differentiable Causal Discovery Under Unmeasured Confounding.
• Causal Modeling with Stochastic Confounders.
• Fair for All: Best-effort Fairness Guarantees for Classification.

BIGDATA 2021

• An Effective, Robust and Fairness-aware Hate Speech Detection Framework.
• Fairness-aware Bandit-based Recommendation.
• ExgFair: A Crowdsourcing Data Exchange Approach To Fair Human Face Datasets Augmentation.
• Bayesian model for Fairness in sampling from clustered data and FP-FN error rates.

CIKM 2021

• CauSeR: Causal Session-based Recommendations for Handling Popularity Bias.
• Certification and Trade-off of Multiple Fairness Criteria in Graph-based Spam Detection.
• Evaluating Fairness in Argument Retrieval.
• Fair Graph Mining.
• FairCORELS, an Open-Source Library for Learning Fair Rule Lists.
• FairER: Entity Resolution With Fairness Constraints.
• Fairness-Aware Training of Decision Trees by Abstract Interpretation.
• Fairness-Aware Unsupervised Feature Selection.
• Fake News, Disinformation, Propaganda, and Media Bias.
• Misbeliefs and Biases in Health-Related Searches.
• Multi-objective Few-shot Learning for Fair Classification.
• Popcorn: Human-in-the-loop Popularity Debiasing in Conversational Recommender Systems.
• SAR-Net: A Scenario-Aware Ranking Network for Personalized Fair Recommendation in Hundreds of Travel Scenarios.
• Towards Reliable and Practicable Algorithmic Recourse.

FAT* 2021

• Price Discrimination with Fairness Constraints.
• Fairness Violations and Mitigation under Covariate Shift.
• Differential Tweetment: Mitigating Racial Dialect Bias in Harmful Tweet Detection.
• Group Fairness: Independence Revisited.
• Towards Fair Deep Anomaly Detection.
• Can You Fake It Until You Make It?: Impacts of Differentially Private Synthetic Data on Downstream Classification Fairness.
• Better Together?: How Externalities of Size Complicate Notions of Solidarity and Actuarial Fairness.
• Evaluating Fairness of Machine Learning Models Under Uncertain and Incomplete Information.
• What We Can’t Measure, We Can’t Understand: Challenges to Demographic Data Procurement in the Pursuit of Fairness.
• Algorithmic Fairness in Predicting Opioid Use Disorder using Machine Learning.
• Leave-one-out Unfairness.
• Fairness, Welfare, and Equity in Personalized Pricing.
• Re-imagining Algorithmic Fairness in India and Beyond.
• This Whole Thing Smacks of Gender: Algorithmic Exclusion in Bioimpedance-based Body Composition Analysis.
• Algorithmic Recourse: from Counterfactual Explanations to Interventions.
• Measurement and Fairness.
• Fairness in Risk Assessment Instruments: Post-Processing to Achieve Counterfactual Equalized Odds.
• An Agent-based Model to Evaluate Interventions on Online Dating Platforms to Decrease Racial Homogamy.
• Socially Fair k-Means Clustering.
• Towards Cross-Lingual Generalization of Translation Gender Bias.
• A Pilot Study in Surveying Clinical Judgments to Evaluate Radiology Report Generation.
• Fairness Through Robustness: Investigating Robustness Disparity in Deep Learning.
• Operationalizing Framing to Support Multiperspective Recommendations of Opinion Pieces.
• Bridging Machine Learning and Mechanism Design towards Algorithmic Fairness.
• Fair Clustering via Equitable Group Representations.
• Fair Classification with Group-Dependent Label Noise.
• Fairness, Equality, and Power in Algorithmic Decision-Making.
• One Label, One Billion Faces: Usage and Consistency of Racial Categories in Computer Vision.
• On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?
• A Statistical Test for Probabilistic Fairness.
• Building and Auditing Fair Algorithms: A Case Study in Candidate Screening.
• I agree with the decision, but they didn’t deserve this: Future Developers’ Perception of Fairness in Algorithmic Decisions.
• Chasing Your Long Tails: Differentially Private Prediction in Health Care Settings.
• Outlining Traceability: A Principle for Operationalizing Accountability in Computing Systems.
• An Action-Oriented AI Policy Toolkit for Technology Audits by Community Advocates and Activists.
• Detecting discriminatory risk through data annotation based on Bayesian inferences.
• The Algorithmic Leviathan: Arbitrariness, Fairness, and Opportunity in Algorithmic Decision Making Systems.
• When the Umpire is also a Player: Bias in Private Label Product Recommendations on E-commerce Marketplaces.

ICDM 2021

• Fair Decision-making Under Uncertainty.
• Promoting Fairness through Hyperparameter Optimization.
• A Multi-view Confidence-calibrated Framework for Fair and Stable Graph Representation Learning.
• Unified Fairness from Data to Learning Algorithm.

ICLR 2021

• SenSeI: Sensitive Set Invariance for Enforcing Individual Fairness.
• Individually Fair Gradient Boosting.
• FairFil: Contrastive Neural Debiasing Method for Pretrained Text Encoders.
• Fair Mixup: Fairness via Interpolation.
• Individually Fair Rankings.
• FairBatch: Batch Selection for Model Fairness.
• INT: An Inequality Benchmark for Evaluating Generalization in Theorem Proving.
• Statistical inference for individual fairness.
• On Dyadic Fairness: Exploring and Mitigating Bias in Graph Connections.

ICML 2021

• Fair Classification with Noisy Protected Attributes: A Framework with Provable Guarantees.
• Fairness and Bias in Online Selection.
• Characterizing Fairness Over the Set of Good Models Under Selective Labels.
• On the Problem of Underranking in Group-Fair Ranking.
• Fairness for Image Generation with Uncertain Sensitive Attributes.
• Fair Selective Classification Via Sufficiency.
• Ditto: Fair and Robust Federated Learning Through Personalization.
• Approximate Group Fairness for Clustering.
• Blind Pareto Fairness and Subgroup Robustness.
• Testing Group Fairness via Optimal Transport Projections.
• Collaborative Bayesian Optimization with Fair Regret.
• Fairness of Exposure in Stochastic Bandits.
• To be Robust or to be Fair: Towards Fairness in Adversarial Training.

IJCAI 2021

• Bias Silhouette Analysis: Towards Assessing the Quality of Bias Metrics for Word Embedding Models.
• Decision Making with Differential Privacy under a Fairness Lens.
• An Examination of Fairness of AI Models for Deepfake Detection.
• Controlling Fairness and Bias in Dynamic Learning-to-Rank (Extended Abstract).

KDD 2021

• Towards Model-Agnostic Post-Hoc Adjustment for Balancing Ranking Fairness and Algorithm Utility.
• Individual Fairness for Graph Neural Networks: A Ranking based Approach.
• Maxmin-Fair Ranking: Individual Fairness under Group-Fairness Constraints.
• Federated Adversarial Debiasing for Fair and Transferable Representations.
• Explaining Algorithmic Fairness Through Fairness-Aware Causal Path Decomposition.
• Deep Clustering based Fair Outlier Detection.
• Deconfounded Recommendation for Alleviating Bias Amplification.
• Understanding and Improving Fairness-Accuracy Trade-offs in Multi-Task Learning.
• Fairness-Aware Online Meta-learning.

NIPS 2021

• A Biased Graph Neural Network Sampler with Near-Optimal Regret.
• A Unified Approach to Fair Online Learning via Blackwell Approachability.
• Adaptive Sampling for Minimax Fair Classification.
• Are My Deep Learning Systems Fair? An Empirical Study of Fixed-Seed Training.
• Assessing Fairness in the Presence of Missing Data.
• Better Algorithms for Individually Fair k-Clustering.
• Bias Out-of-the-Box: An Empirical Analysis of Intersectional Occupational Biases in Popular Generative Language Models.
• Characterizing the risk of fairwashing.
• DECAF: Generating Fair Synthetic Data Using Causally-Aware Generative Networks.
• Differentially Private Empirical Risk Minimization under the Fairness Lens.
• Does enforcing fairness mitigate biases caused by subpopulation shift?
• Efficient First-Order Contextual Bandits: Prediction, Allocation, and Triangular Discrimination.
• Fair Algorithms for Multi-Agent Multi-Armed Bandits.
• Fair Classification with Adversarial Perturbations.
• Fair Clustering Under a Bounded Cost.
• Fair Exploration via Axiomatic Bargaining.
• Fair Sequential Selection Using Supervised Learning Models.
• Fair Sparse Regression with Clustering: An Invex Relaxation for a Combinatorial Problem.
• Fairness in Ranking under Uncertainty.
• Fairness via Representation Neutralization.
• FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout.
• Gradient Driven Rewards to Guarantee Fairness in Collaborative Machine Learning.
• Learning Models for Actionable Recourse.
• Post-processing for Individual Fairness.
• Retiring Adult: New Datasets for Fair Machine Learning.
• Sample Selection for Fair and Robust Training.
• Scalable and Stable Surrogates for Flexible Classifiers with Fairness Constraints.
• Semi-Supervised Semantic Segmentation via Adaptive Equalization Learning.
• Subgroup Generalization and Fairness of Graph Neural Networks.
• Towards Robust and Reliable Algorithmic Recourse.
• Two-sided fairness in rankings via Lorenz dominance.
• Visual Search Asymmetry: Deep Nets and Humans Share Similar Inherent Biases.

SDM 2021

• Fairness-aware Agnostic Federated Learning.
• Fair Classification Under Strict Unawareness.

UAI 2021

• Addressing fairness in classification with a model-agnostic multi-objective algorithm.
• Towards a unified framework for fair and stable graph representation learning.

WWW 2021

• AI Principles in Identifying Toxicity in Online Conversation: Keynote at the Third Workshop on Fairness, Accountability, Transparency, Ethics and Society on the Web.
• Auditing for Discrimination in Algorithms Delivering Job Ads.
• Auditing Source Diversity Bias in Video Search Results Using Virtual Agents.
• Automating Fairness Configurations for Machine Learning.
• Debiasing Career Recommendations with Neural Fair Collaborative Filtering.
• Does Gender Matter in the News? Detecting and Examining Gender Bias in News Articles.
• Estimation of Fair Ranking Metrics with Incomplete Judgments.
• Fair and Representative Subset Selection from Data Streams.
• Fair Partitioning of Public Resources: Redrawing District Boundary to Minimize Spatial Inequality in School Funding.
• Fairness beyond “equal”: The Diversity Searcher as a Tool to Detect and Enhance the Representation of Socio-political Actors in News Media.
• Fairness-Aware PageRank.
• How Fair is Fairness-aware Representative Ranking?
• Learning Fair Representations for Recommendation: A Graph-based Perspective.
• Maximizing Marginal Fairness for Dynamic Learning to Rank.
• Mitigating Gender Bias in Captioning Systems.
• Towards Ongoing Detection of Linguistic Bias on Wikipedia.
• Understanding User Sensemaking in Machine Learning Fairness Assessment Systems.
• User-oriented Fairness in Recommendation.

Others 2021

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WSDM 2021

• Popularity-Opportunity Bias in Collaborative Filtering.
• Deconfounding with Networked Observational Data in a Dynamic Environment.
• Causal Transfer Random Forest: Combining Logged Data and Randomized Experiments for Robust Prediction.
• Split-Treatment Analysis to Rank Heterogeneous Causal Effects for Prospective Interventions.
• Explain and Predict, and then Predict Again.
• Practical Compositional Fairness: Understanding Fairness in Multi-Component Recommender Systems.
• Towards Long-term Fairness in Recommendation.
• Unifying Online and Counterfactual Learning to Rank: A Novel Counterfactual Estimator that Effectively Utilizes Online Interventions.
• Interpretable Ranking with Generalized Additive Models.

COLT 2021

• Approximation Algorithms for Socially Fair Clustering.

2020

AAAI 2020

• Faking Fairness via Stealthily Biased Sampling.
• Differentially Private and Fair Classification via Calibrated Functional Mechanism.
• Bursting the Filter Bubble: Fairness-Aware Network Link Prediction.
• Making Existing Clusterings Fairer: Algorithms, Complexity Results and Insights.
• Fairness in Network Representation by Latent Structural Heterogeneity in Observational Data.
• Pairwise Fairness for Ranking and Regression.
• Achieving Fairness in the Stochastic Multi-Armed Bandit Problem.
• Fairness for Robust Log Loss Classification.
• Learning Fair Naive Bayes Classifiers by Discovering and Eliminating Discrimination Patterns.

AISTATS 2020

• Learning Fair Representations for Kernel Models.
• Fair Decisions Despite Imperfect Predictions.
• Optimized Score Transformation for Fair Classification.
• Equalized odds postprocessing under imperfect group information.
• Fairness Evaluation in Presence of Biased Noisy Labels.
• Fair Correlation Clustering.
• Auditing ML Models for Individual Bias and Unfairness.

BIGDATA 2020

• BeFair: Addressing Fairness in the Banking Sector.
• FairFL: A Fair Federated Learning Approach to Reducing Demographic Bias in Privacy-Sensitive Classification Models.
• Fairness for Whom? Understanding the Reader’s Perception of Fairness in Text Summarization.
• Fairness Metrics: A Comparative Analysis.
• How biased are American media outlets? A framework for presentation bias regression.

CIKM 2020

• Spectral Relaxations and Fair Densest Subgraphs.
• Fair Class Balancing: Enhancing Model Fairness without Observing Sensitive Attributes.
• Active Query of Private Demographic Data for Learning Fair Models.
• Fairness-Aware Learning with Prejudice Free Representations.
• Denoising Individual Bias for Fairer Binary Submatrix Detection.
• LiFT: A Scalable Framework for Measuring Fairness in ML Applications.

FAT* 2020

• Closing the AI accountability gap: defining an end-to-end framework for internal algorithmic auditing.
• Toward situated interventions for algorithmic equity: lessons from the field.
• “The human body is a black box”: supporting clinical decision-making with deep learning.
• Assessing algorithmic fairness with unobserved protected class using data combination.
• FlipTest: fairness testing via optimal transport.
• Implications of AI (un-)fairness in higher education admissions: the effects of perceived AI (un-)fairness on exit, voice and organizational reputation.
• Auditing radicalization pathways on YouTube.
• Case study: predictive fairness to reduce misdemeanor recidivism through social service interventions.
• The concept of fairness in the GDPR: a linguistic and contextual interpretation.
• Studying up: reorienting the study of algorithmic fairness around issues of power.
• Fair decision making using privacy-protected data.
• Fairness warnings and fair-MAML: learning fairly with minimal data.
• Algorithmic targeting of social policies: fairness, accuracy, and distributed governance.
• The philosophical basis of algorithmic recourse.
• Bidding strategies with gender nondiscrimination constraints for online ad auctions.
• Multi-category fairness in sponsored search auctions.
• Reducing sentiment polarity for demographic attributes in word embeddings using adversarial learning.
• Interventions for ranking in the presence of implicit bias.
• The disparate equilibria of algorithmic decision making when individuals invest rationally.
• An empirical study on the perceived fairness of realistic, imperfect machine learning models.
• Recommendations and user agency: the reachability of collaboratively-filtered information.
• Bias in word embeddings.
• What does it mean to ‘solve’ the problem of discrimination in hiring?: social, technical and legal perspectives from the UK on automated hiring systems.
• Mitigating bias in algorithmic hiring: evaluating claims and practices.
• The impact of overbooking on a pre-trial risk assessment tool.
• Awareness in practice: tensions in access to sensitive attribute data for antidiscrimination.
• Towards a critical race methodology in algorithmic fairness.
• On the apparent conflict between individual and group fairness.
• Fairness is not static: deeper understanding of long term fairness via simulation studies.
• Fair classification and social welfare.
• Preference-informed fairness.
• Towards fairer datasets: filtering and balancing the distribution of the people subtree in the ImageNet hierarchy.
• The case for voter-centered audits of search engines during political elections.
• Whose tweets are surveilled for the police: an audit of a social-media monitoring tool via log files.
• Dirichlet uncertainty wrappers for actionable algorithm accuracy accountability and auditability.
• Counterfactual risk assessments, evaluation, and fairness.
• The false promise of risk assessments: epistemic reform and the limits of fairness.
• The effects of competition and regulation on error inequality in data-driven markets.

ICDM 2020

• A Primal-Dual Subgradient Approach for Fair Meta Learning.
• FairMixRep: Self-supervised Robust Representation Learning for Heterogeneous Data with Fairness constraints.
• Fairness Perception from a Network-Centric Perspective.
• Metric-Free Individual Fairness with Cooperative Contextual Bandits.

ICML 2020

• A Pairwise Fair and Community-preserving Approach to k-Center Clustering.
• How to Solve Fair k-Center in Massive Data Models.
• Fair Generative Modeling via Weak Supervision.
• Causal Modeling for Fairness In Dynamical Systems.
• Is There a Trade-Off Between Fairness and Accuracy? A Perspective Using Mismatched Hypothesis Testing.
• FACT: A Diagnostic for Group Fairness Trade-offs.
• Too Relaxed to Be Fair.
• Individual Fairness for k-Clustering.
• Minimax Pareto Fairness: A Multi Objective Perspective.
• Fair Learning with Private Demographic Data.
• Two Simple Ways to Learn Individual Fairness Metrics from Data.
• FR-Train: A Mutual Information-Based Approach to Fair and Robust Training.
• Bounding the fairness and accuracy of classifiers from population statistics.
• Measuring Non-Expert Comprehension of Machine Learning Fairness Metrics.
• Learning Fair Policies in Multi-Objective (Deep) Reinforcement Learning with Average and Discounted Rewards.

IJCAI 2020

• WEFE: The Word Embeddings Fairness Evaluation Framework.
• Individual Fairness Revisited: Transferring Techniques from Adversarial Robustness.
• Achieving Outcome Fairness in Machine Learning Models for Social Decision Problems.
• Relation-Based Counterfactual Explanations for Bayesian Network Classifiers.
• Metamorphic Testing and Certified Mitigation of Fairness Violations in NLP Models.
• Fairness-Aware Neural Rényi Minimization for Continuous Features.
• FNNC: Achieving Fairness through Neural Networks.
• Adversarial Graph Embeddings for Fair Influence Maximization over Social Networks.

KDD 2020

• InFoRM: Individual Fairness on Graph Mining.
• Towards Fair Truth Discovery from Biased Crowdsourced Answers.
• Evaluating Fairness Using Permutation Tests.
• A Causal Look at Statistical Definitions of Discrimination.
• List-wise Fairness Criterion for Point Processes.
• Algorithmic Decision Making with Conditional Fairness.

NIPS 2020

• Achieving Equalized Odds by Resampling Sensitive Attributes.
• Fairness without Demographics through Adversarially Reweighted Learning.
• Fairness with Overlapping Groups; a Probabilistic Perspective.
• Robust Optimization for Fairness with Noisy Protected Groups.
• Fair regression with Wasserstein barycenters.
• Learning Certified Individually Fair Representations.
• Metric-Free Individual Fairness in Online Learning.
• Fairness constraints can help exact inference in structured prediction.
• Investigating Gender Bias in Language Models Using Causal Mediation Analysis.
• Probabilistic Fair Clustering.
• KFC: A Scalable Approximation Algorithm for $k$-center Fair Clustering.
• A Fair Classifier Using Kernel Density Estimation.
• Exploiting MMD and Sinkhorn Divergences for Fair and Transferable Representation Learning.
• Fair Multiple Decision Making Through Soft Interventions.
• Ensuring Fairness Beyond the Training Data.
• How do fair decisions fare in long-term qualification?
• Can I Trust My Fairness Metric? Assessing Fairness with Unlabeled Data and Bayesian Inference.
• Fair regression via plug-in estimator and recalibration with statistical guarantees.
• Fair Hierarchical Clustering.

SDM 2020

• Bayesian Modeling of Intersectional Fairness: The Variance of Bias.
• On the Information Unfairness of Social Networks.

UAI 2020

• Fair Contextual Multi-Armed Bandits: Theory and Experiments.
• Towards Threshold Invariant Fair Classification.
• Verifying Individual Fairness in Machine Learning Models.

WWW 2020

• FairRec: Two-Sided Fairness for Personalized Recommendations in Two-Sided Platforms.
• Designing Fairly Fair Classifiers Via Economic Fairness Notions.
• Learning Model-Agnostic Counterfactual Explanations for Tabular Data.

Others 2020

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ASONAM 2020

2019

AAAI 2019

• Learning to Address Health Inequality in the United States with a Bayesian Decision Network
• Convex Formulations for Fair Principal Component Analysis
• Bayesian Fairness
• One-Network Adversarial Fairness
• Eliminating Latent Discrimination: Train Then Mask
• Path-Specific Counterfactual Fairness

AISTATS 2019

• Learning Controllable Fair Representations

BIGDATA 2019

• FAE: A Fairness-Aware Ensemble Framework
• Privacy Bargaining with Fairness: Privacy–Price Negotiation System for Applying Differential Privacy in Data Market Environments
• FairGAN+: Achieving Fair Data Generation and Classification through Generative Adversarial Nets

CIKM 2019

• AdaFair: Cumulative Fairness Adaptive Boosting

FAT* 2019

• Fairness and Abstraction in Sociotechnical Systems
• 50 Years of Test (Un)fairness: Lessons for Machine Learning
• A comparative study of fairness-enhancing interventions in machine learning
• Analyzing Biases in Perception of Truth in News Stories and their Implications for Fact Checking
• Disparate Interactions: An Algorithm-in-the-Loop Analysis of Fairness in Risk Assessments
• Problem Formulation and Fairness
• Fairness under unawareness: assessing disparity when protected class is unobserved
• Actionable Recourse in Linear Classification
• A Taxonomy of Ethical Tensions in Inferring Mental Health States from Social Media
• The Disparate Effects of Strategic Manipulation
• Racial categories in machine learning
• Fairness through Causal Awareness: Learning Causal Latent-Variable Models for Biased Data
• An Empirical Study of Rich Subgroup Fairness for Machine Learning
• From Soft Classifiers to Hard Decisions: How fair can we be?
• Efficient Search for Diverse Coherent Explanations
• A Moral Framework for Understanding Fair ML through Economic Models of Equality of Opportunity
• Classification with Fairness Constraints: A Meta-Algorithm with Provable Guarantees
• Access to Population-Level Signaling as a Source of Inequality
• Measuring the Biases that Matter: The Ethical and Casual Foundations for Measures of Fairness in Algorithms
• Fairness-Aware Programming
• Clear Sanctions, Vague Rewards: How China’s Social Credit System Defines “Good” and “Bad” Behavior
• On Microtargeting Socially Divisive Ads: A Case Study of Russia-Linked Ad Campaigns on Facebook
• Dissecting Racial Bias in an Algorithm that Guides Health Decisions for 70 million people
• SIREN: A Simulation Framework for Understanding the Effects of Recommender Systems in Online News Environments
• Equality of Voice: Towards Fair Representation in Crowdsourced Top-K Recommendations
• From Fair Decision Making To Social Equality

ICDM 2019

• Fair Adversarial Gradient Tree Boosting
• Rank-Based Multi-task Learning For Fair Regression
• A Distributed Fair Machine Learning Framework with Private Demographic Data Protection

ICML 2019

• Fair Regression: Quantitative Definitions and Reduction-Based Algorithms
• Fairwashing: the risk of rationalization
• Scalable Fair Clustering
• Compositional Fairness Constraints for Graph Embeddings
• Understanding the Origins of Bias in Word Embeddings
• Proportionally Fair Clustering
• Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints
• Flexibly Fair Representation Learning by Disentanglement
• Obtaining Fairness using Optimal Transport Theory
• On the Long-term Impact of Algorithmic Decision Policies: Effort Unfairness and Feature Segregation through Social Learning
• Stable and Fair Classification
• Differentially Private Fair Learning
• Fair k-Center Clustering for Data Summarization
• Guarantees for Spectral Clustering with Fairness Constraints
• Making Decisions that Reduce Discriminatory Impacts
• The Implicit Fairness Criterion of Unconstrained Learning
• Fairness-Aware Learning for Continuous Attributes and Treatments
• Toward Controlling Discrimination in Online Ad Auctions
• Learning Optimal Fair Policies
• Fairness without Harm: Decoupled Classifiers with Preference Guarantees
• Repairing without Retraining: Avoiding Disparate Impact with Counterfactual Distributions
• Fairness risk measures

IJCAI 2019

• Counterfactual Fairness: Unidentification, Bound and Algorithm
• Achieving Causal Fairness through Generative Adversarial Networks
• FAHT: An Adaptive Fairness-aware Decision Tree Classifier
• Delayed Impact of Fair Machine Learning
• The Price of Local Fairness in Multistage Selection

KDD 2019

• Fairness in Recommendation Ranking through Pairwise Comparisons
• Fairness-Aware Ranking in Search & Recommendation Systems with Application to LinkedIn Talent Search
• Mathematical Notions vs. Human Perception of Fairness: A Descriptive Approach to Fairness for Machine Learning

NIPS 2019

• Noise-tolerant fair classification
• Envy-Free Classification
• Discrimination in Online Markets: Effects of Social Bias on Learning from Reviews and Policy Design
• PC-Fairness: A Unified Framework for Measuring Causality-based Fairness
• Assessing Disparate Impact of Personalized Interventions: Identifiability and Bounds
• The Fairness of Risk Scores Beyond Classification: Bipartite Ranking and the XAUC Metric
• Fair Algorithms for Clustering
• Policy Learning for Fairness in Ranking
• Average Individual Fairness: Algorithms, Generalization and Experiments
• Paradoxes in Fair Machine Learning
• Unlocking Fairness: a Trade-off Revisited
• Equal Opportunity in Online Classification with Partial Feedback
• Learning Fairness in Multi-Agent Systems
• On the Fairness of Disentangled Representations
• Differential Privacy Has Disparate Impact on Model Accuracy
• Inherent Tradeoffs in Learning Fair Representations
• Exploring Algorithmic Fairness in Robust Graph Covering Problems
• Leveraging Labeled and Unlabeled Data for Consistent Fair Binary Classification
• Assessing Social and Intersectional Biases in Contextualized Word Representations
• Offline Contextual Bandits with High Probability Fairness Guarantees
• Multi-Criteria Dimensionality Reduction with Applications to Fairness
• Group Retention when Using Machine Learning in Sequential Decision Making: the Interplay between User Dynamics and Fairness

SDM 2019

• Fairness in representation: quantifying stereotyping as a representational harm

UAI 2019

• The Sensitivity of Counterfactual Fairness to Unmeasured Confounding
• Wasserstein Fair Classification

WWW 2019

• Fairness in Algorithmic Decision Making: An Excursion Through the Lens of Causality
• FARE: Diagnostics for Fair Ranking using Pairwise Error Metrics
• On Convexity and Bounds of Fairness-aware Classification

Others 2019

• Fighting Fire with Fire: Using Antidote Data to Improve Polarization and Fairness of Recommender Systems, WSDM 2019
• Interventional Fairness: Causal Database Repair for Algorithmic Fairness., SIGMOD 2019
• Designing Fair Ranking Schemes., SIGMOD 2019

2018

AAAI 2018

• Non-Discriminatory Machine Learning through Convex Fairness Criteria
• Knowledge, Fairness, and Social Constraints
• Fairness in Decision-Making – The Causal Explanation Formula
• Fair Inference on Outcomes
• Beyond Distributive Fairness in Algorithmic Decision Making: Feature Selection for Procedurally Fair Learning
• Balancing Lexicographic Fairness and a Utilitarian Objective with Application to Kidney Exchange

AISTATS 2018

• Fast Threshold Tests for Detecting Discrimination
• Spectral Algorithms for Computing Fair Support Vector Machines

BIGDATA 2018

• FairGAN: Fairness-aware Generative Adversarial Networks

CIKM 2018

• Fairness-Aware Tensor-Based Recommendation
• Towards a Fair Marketplace: Counterfactual Evaluation of the trade-off between Relevance, Fairness & Satisfaction in Recommendation Systems

FAT* 2018

• Potential for Discrimination in Online Targeted Advertising
• Discrimination in Online Personalization: A Multidisciplinary Inquiry
• Privacy for All: Ensuring Fair and Equitable Privacy Protections
• Interventions over Predictions: Reframing the Ethical Debate for Actuarial Risk Assessment
• Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification
• Analyze, Detect and Remove Gender Stereotyping from Bollywood Movies
• The cost of fairness in binary classification
• Decoupled Classifiers for Group-Fair and Efficient Machine Learning
• Fairness in Machine Learning: Lessons from Political Philosophy
• All The Cool Kids, How Do They Fit In?: Popularity and Demographic Biases in Recommender Evaluation and Effectiveness
• Recommendation Independence
• Balanced Neighborhoods for Multi-sided Fairness in Recommendation

ICDM 2018

• Using Balancing Terms to Avoid Discrimination in Classification

ICML 2018

• Blind Justice: Fairness with Encrypted Sensitive Attributes
• Scalable Deletion-Robust Submodular Maximization: Data Summarization with Privacy and Fairness Constraints
• Nonconvex Optimization for Fair Regression
• Fair and Diverse DPP-based Data Summarization
• Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness
• Residual Unfairness in Fair Machine Learning from Prejudiced Data
• A Reductions Approach to Fair Classification
• Probably Approximately Metric-Fair Learning
• Learning Adversarially Fair and Transferable Representations
• Fairness Without Demographics in Repeated Loss Minimization, Best Paper Runner Up Awards

IJCAI 2018

• Achieving Non-Discrimination in Prediction
• Preventing Disparate Treatment in Sequential Decision Making

KDD 2018

• Fairness of Exposure in Rankings
• On Discrimination Discovery and Removal in Ranked Data using Causal Graph
• A Unified Approach to Quantifying Algorithmic Unfairness: Measuring Individual & Group Unfairness via Inequality Indices

NIPS 2018

• Fairness Behind a Veil of Ignorance: a Welfare Analysis for Automated Decision Making
• Enhancing the Accuracy and Fairness of Human Decision Making
• Online Learning with an Unknown Fairness Metric
• Empirical Risk Minimization under Fairness Constraints
• Why Is My Classifier Discriminatory
• Hunting for Discriminatory Proxies in Linear Regression Models
• Fairness Through Computationally Bounded Awareness
• Predict Responsibly Improving Fairness and Accuracy by Learning to Defer
• On Preserving Non Discrimination When Combining Expert Advice
• The Price of Fair PCA: One Extra Dimension
• Equality of Opportunity in Classification: A Causal Approach
• Invariant Representations without Adversarial Training
• Learning to Pivot with Adversarial Networks

SDM 2018

null

UAI 2018

null

WWW 2018

• Adaptive Sensitive Reweighting to Mitigate Bias in Fairness-aware Classification
• Human Perceptions of Fairness in Algorithmic Decision Making: A Case Study of Criminal Risk Prediction

Others 2018

• Biases in the Facebook News Feed: a Case Study on the Italian Elections, ASONAM 2018
• Unleashing Linear Optimizers for Group-Fair Learning and Optimization, COLT 2018

2017

AAAI 2017

null

AISTATS 2017

• Fairness Constraints: Mechanisms for Fair Classification, supplement

BIGDATA 2017

• Discrimination detection by causal effect estimation

CIKM 2017

• FA*IR: A Fair Top-k Ranking Algorithm

FAT* 2017

null

ICDM 2017

• Identifying Media Bias by Analyzing Reported Speech.

ICML 2017

• Fairness in Reinforcement Learning
• Meritocratic Fairness for Cross-Population Selection

IJCAI 2017

• A Causal Framework for Discovering and Removing Direct and Indirect Discrimination

KDD 2017

• Algorithmic decision making and the cost of fairness
• Achieving Non-Discrimination in Data Release

NIPS 2017

• From Parity to Preference-based Notions of Fairness in Classification
• Controllable Invariance through Adversarial Feature Learning
• Avoiding Discrimination through Causal Reasoning
• Beyond Parity: Fairness Objectives for Collaborative Filtering
• Optimized Pre-Processing for Discrimination Prevention
• Counterfactual Fairness
• Fair Clustering Through Fairlets
• On Fairness and Calibration
• When Worlds Collide: Integrating Different Counterfactual Assumptions in Fairness

SDM 2017

null

UAI 2017

• Importance Sampling for Fair Policy Selection, supplement

WWW 2017

• Fairness in Package-to-Group Recommendations
• Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment

Others 2017

• Learning Non-Discriminatory Predictors, COLT 2017
• Inherent Trade-Offs in the Fair Determination of Risk Scores, ITCS 2017

2016

AAAI 2016

AISTATS 2016

BIGDATA 2016

null

CIKM 2016

null

FAT* 2016

null

ICDM 2016

• Auditing Black-Box Models for Indirect Influence

ICML 2016

• Recommendations as Treatments: Debiasing Learning and Evaluation.

IJCAI 2016

• Situation Testing-Based Discrimination Discovery: A Causal Inference Approach

KDD 2016

• Algorithmic Bias: From Discrimination Discovery to Fairness-aware Data Mining.

NIPS 2016

• Fairness in Learning: Classic and Contextual Bandits
• Equality of Opportunity in Supervised Learning
• Satisfying Real-world Goals with Dataset Constraints
• Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings

SDM 2016

• A Confidence-Based Approach for Balancing Fairness and Accuracy

UAI 2016

null

WWW 2016

null

Others 2016

• A KDD Process for Discrimination Discovery, ECML/PKDD 2016

2015

AAAI 2015

• Fair Information Sharing for Treasure Hunting.
• On Fairness in Decision-Making under Uncertainty: Definitions, Computation, and Comparison.

AISTATS 2015

null

BIGDATA 2015

null

CIKM 2015

• BiasWatch: A Lightweight System for Discovering and Tracking Topic-Sensitive Opinion Bias in Social Media.

FAT* 2015

null

ICDM 2015

ICML 2015

null

IJCAI 2015

• Quantifying Robustness of Trust Systems against Collusive Unfair Rating Attacks Using Information Theory.
• Re-Ranking Voting-Based Answers by Discarding User Behavior Biases.

KDD 2015

• Certifying and Removing Disparate Impact

NIPS 2015

SDM 2015

null

UAI 2015

null

WWW 2015

• Measuring Gender Bias in News Images.

2014

• Fair pattern discovery, SAC 2014
• Anti-discrimination Analysis Using Privacy Attack Strategies, ECML/PKDD 2014

2013

• Learning Fair Representations, ICML 2013
• Discrimination aware classification for imbalanced datasets, CIKM 2013

2012

• Fairness-Aware Classifier with Prejudice Remover Regularizer, ECML/PKDD 2012
• Fairness through awareness, ITCS 2012
• Decision theory for discrimination-aware classification, ICDM 2012
• A study of top-k measures for discrimination discovery, SAC 2012

2011

• k-NN as an implementation of situation testing for discrimination discovery and prevention, KDD 2011
• Handling Conditional Discrimination, ICDM 2011
• Discrimination prevention in data mining for intrusion and crime detection, CICS 2011

2010

• Discrimination Aware Decision Tree Learning, ICDM 2010
• Classification with no discrimination by preferential sampling, 19th Machine Learning Conf. Belgium and The Netherlands 2010

2009

• Measuring Discrimination in Socially-Sensitive Decision Records, SDM 2009
• Classifying without discriminating, IC4 2009

2008

• Discrimination-aware data mining, KDD 2008