Conference Papers
2022
AAAI 2022
- A Random CNN Sees Objects: One Inductive Bias of CNN and Its Applications.
- Resistance Training Using Prior Bias: Toward Unbiased Scene Graph Generation.
- Unbiased IoU for Spherical Image Object Detection.
- LAGConv: Local-Context Adaptive Convolution Kernels with Global Harmonic Bias for Pansharpening.
- A Causal Debiasing Framework for Unsupervised Salient Object Detection.
- Debiased Batch Normalization via Gaussian Process for Generalizable Person Re-identification.
- Information-Theoretic Bias Reduction via Causal View of Spurious Correlation.
- Cross-Domain Empirical Risk Minimization for Unbiased Long-Tailed Classification.
- Unifying Knowledge Base Completion with PU Learning to Mitigate the Observation Bias.
- Locally Fair Partitioning.
- Fair and Truthful Giveaway Lotteries.
- Truthful and Fair Mechanisms for Matroid-Rank Valuations.
- A Little Charity Guarantees Fair Connected Graph Partitioning.
- Weighted Fairness Notions for Indivisible Items Revisited.
- Fair and Efficient Allocations of Chores under Bivalued Preferences.
- Improved Maximin Guarantees for Subadditive and Fractionally Subadditive Fair Allocation Problem.
- On Testing for Discrimination Using Causal Models.
- Online Certification of Preference-Based Fairness for Personalized Recommender Systems.
- Modification-Fair Cluster Editing.
- Recovering the Propensity Score from Biased Positive Unlabeled Data.
- Achieving Counterfactual Fairness for Causal Bandit.
- Group-Aware Threshold Adaptation for Fair Classification.
- Spatial Frequency Bias in Convolutional Generative Adversarial Networks.
- A Computable Definition of the Spectral Bias.
- Gradient Based Activations for Accurate Bias-Free Learning.
- Fast and Efficient MMD-Based Fair PCA via Optimization over Stiefel Manifold.
- Covered Information Disentanglement: Model Transparency via Unbiased Permutation Importance.
- On the Impossibility of Non-trivial Accuracy in Presence of Fairness Constraints.
- Powering Finetuning in Few-Shot Learning: Domain-Agnostic Bias Reduction with Selected Sampling.
- Controlling Underestimation Bias in Reinforcement Learning via Quasi-median Operation.
- Cooperative Multi-Agent Fairness and Equivariant Policies.
- Why Fair Labels Can Yield Unfair Predictions: Graphical Conditions for Introduced Unfairness.
- Towards Debiasing DNN Models from Spurious Feature Influence.
- 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.
- Mitigating Reporting Bias in Semi-supervised Temporal Commonsense Inference with Probabilistic Soft Logic.
- Attention Biasing and Context Augmentation for Zero-Shot Control of Encoder-Decoder Transformers for Natural Language Generation.
- KATG: Keyword-Bias-Aware Adversarial Text Generation for Text Classification.
- Debiasing NLU Models via Causal Intervention and Counterfactual Reasoning.
- 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.
- Target Languages (vs. Inductive Biases) for Learning to Act and Plan.
- 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.
- Deep Representation Debiasing via Mutual Information Minimization and Maximization (Student Abstract).
- 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.
- Towards Better Detection of Biased Language with Scarce, Noisy, and Biased Annotations.
- Investigating Debiasing Effects on Classification and Explainability.
- 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.
- A Bio-Inspired Framework for Machine Bias Interpretation.
- Algorithms that “Don’t See Color”: Measuring Biases in Lookalike and Special Ad Audiences.
- From Coded Bias to Existential Threat: Expert Frames and the Epistemic Politics of AI Governance.
- Strategic Best Response Fairness in Fair Machine Learning.
- Explainability’s Gain is Optimality’s Loss?: How Explanations Bias Decision-making.
- Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation.
- Identifying Bias in Data Using Two-Distribution Hypothesis Tests.
- Why is my System Biased?: Rating of AI Systems through a Causal Lens.
- Socially-Aware Artificial Intelligence for Fair Mobility.
- Bias in Artificial Intelligence Models in Financial Services.
- 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.
- Mitigating Biases in Student Performance Prediction via Attention-Based Personalized Federated Learning.
- 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.
- Visual Encoding and Debiasing for CTR Prediction.
- How Does the Crowd Impact the Model? A Tool for Raising Awareness of Social Bias in Crowdsourced Training Data.
FAT* 2022
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
ICML 2022
- Active Sampling for Min-Max Fairness.
- Fair and Fast k-Center Clustering for Data Summarization.
- On the Hidden Biases of Policy Mirror Ascent in Continuous Action Spaces.
- Skin Deep Unlearning: Artefact and Instrument Debiasing in the Context of Melanoma Classification.
- Fairness with Adaptive Weights.
- The Poisson Binomial Mechanism for Unbiased Federated Learning with Secure Aggregation.
- RieszNet and ForestRiesz: Automatic Debiased Machine Learning with Neural Nets and Random Forests.
- Mitigating Gender Bias in Face Recognition using the von Mises-Fisher Mixture Model.
- Fair Generalized Linear Models with a Convex Penalty.
- Fast rates for noisy interpolation require rethinking the effect of inductive bias.
- Inductive Biases and Variable Creation in Self-Attention Mechanisms.
- Contrastive Mixture of Posteriors for Counterfactual Inference, Data Integration and Fairness.
- Unsupervised Detection of Contextualized Embedding Bias with Application to Ideology.
- Input-agnostic Certified Group Fairness via Gaussian Parameter Smoothing.
- Learning fair representation with a parametric integral probability metric.
- Implicit Bias of Linear Equivariant Networks.
- Achieving Fairness at No Utility Cost via Data Reweighing with Influence.
- Fluctuations, Bias, Variance & Ensemble of Learners: Exact Asymptotics for Convex Losses in High-Dimension.
- ModLaNets: Learning Generalisable Dynamics via Modularity and Physical Inductive Bias.
- Rethinking Fano’s Inequality in Ensemble Learning.
- Implicit Bias of the Step Size in Linear Diagonal Neural Networks.
- The Primacy Bias in Deep Reinforcement Learning.
- Causal Conceptions of Fairness and their Consequences.
- Debiaser Beware: Pitfalls of Centering Regularized Transport Maps.
- A Convergence Theory for SVGD in the Population Limit under Talagrand’s Inequality T1.
- Understanding Contrastive Learning Requires Incorporating Inductive Biases.
- Selective Regression under Fairness Criteria.
- Metric-Fair Active Learning.
- Fair Representation Learning through Implicit Path Alignment.
IJCAI 2022
- Individual Fairness Guarantees for Neural Networks.
- How Does Frequency Bias Affect the Robustness of Neural Image Classifiers against Common Corruption and Adversarial Perturbations?
- 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
- Avoiding Biases due to Similarity Assumptions in Node Embeddings.
- Scalar is Not Enough: Vectorization-based Unbiased Learning to Rank.
- A Generalized Doubly Robust Learning Framework for Debiasing Post-Click Conversion Rate Prediction.
- 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.
- Fair Ranking as Fair Division: Impact-Based Individual Fairness in Ranking.
- Balancing Bias and Variance for Active Weakly Supervised Learning.
- 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.
- CausalMTA: Eliminating the User Confounding Bias for Causal Multi-touch Attribution.
- Deconfounding Duration Bias in Watch-time Prediction for Video Recommendation.
- Why Data Scientists Prefer Glassbox Machine Learning: Algorithms, Differential Privacy, Editing and Bias Mitigation.
- 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
- A Large Scale Search Dataset for Unbiased Learning to Rank.
- Counterfactual Fairness with Partially Known Causal Graph.
- Adaptive Data Debiasing through Bounded Exploration.
- A Reduction to Binary Approach for Debiasing Multiclass Datasets.
- Combinatorial Bandits with Linear Constraints: Beyond Knapsacks and Fairness.
- Debiased Machine Learning without Sample-Splitting for Stable Estimators.
- Is Sortition Both Representative and Fair?
- Fairness in Federated Learning via Core-Stability.
- Spectral Bias in Practice: The Role of Function Frequency in Generalization.
- 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.
- SelecMix: Debiased Learning by Contradicting-pair Sampling.
- 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.
- Learning Debiased Classifier with Biased Committee.
- 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.
- Debiased Self-Training for Semi-Supervised Learning.
- 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?
- Turning the Tables: Biased, Imbalanced, Dynamic Tabular Datasets for ML Evaluation.
- Optimal Transport of Classifiers to Fairness.
- On Learning Fairness and Accuracy on Multiple Subgroups.
- Fairness Reprogramming.
- Implicit Bias of Gradient Descent on Reparametrized Models: On Equivalence to Mirror Descent.
- Beyond Adult and COMPAS: Fair Multi-Class Prediction via Information Projection.
- Fair and Optimal Decision Trees: A Dynamic Programming Approach.
SDM 2022
UAI 2022
- Active approximately metric-fair learning.
- Quadratic metric elicitation for fairness and beyond.
- Efficient resource allocation with fairness constraints in restless multi-armed bandits.
- 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.
- Unbiased Graph Embedding with Biased Graph Observations.
- Rating Distribution Calibration for Selection Bias Mitigation in Recommendations.
- UKD: Debiasing Conversion Rate Estimation via Uncertainty-regularized Knowledge Distillation.
- Unbiased Sequential Recommendation with Latent Confounders.
- CBR: Context Bias aware Recommendation for Debiasing User Modeling and Click Prediction✱.
- Cross Pairwise Ranking for Unbiased Item Recommendation.
- 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.
- Scheduling Virtual Conferences Fairly: Achieving Equitable Participant and Speaker Satisfaction.
- 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
WSDM 2022
- k-Clustering with Fair Outliers.
- Toward Pareto Efficient Fairness-Utility Trade-off in Recommendation through Reinforcement Learning.
- It Is Different When Items Are Older: Debiasing Recommendations When Selection Bias and User Preferences Are Dynamic.
- 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 Unbiased and Robust Causal Ranking for Recommender Systems.
- Assessing Algorithmic Biases for Musical Version Identification.
- 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.
- Maximin Fairness with Mixed Divisible and Indivisible Goods.
- 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
TBD
FAT* 2021
- Black Feminist Musings on Algorithmic Oppression.
- Price Discrimination with Fairness Constraints.
- Fairness Violations and Mitigation under Covariate Shift.
- Reasons, Values, Stakeholders: A Philosophical Framework for Explainable Artificial Intelligence.
- Allocating Opportunities in a Dynamic Model of Intergenerational Mobility.
- Corporate Social Responsibility via Multi-Armed Bandits.
- Biases in Generative Art: A Causal Look from the Lens of Art History.
- Designing an Online Infrastructure for Collecting AI Data From People With Disabilities.
- Fifty Shades of Grey: In Praise of a Nuanced Approach Towards Trustworthy Design.
- Representativeness in Statistics, Politics, and Machine Learning.
- The Distributive Effects of Risk Prediction in Environmental Compliance: Algorithmic Design, Environmental Justice, and Public Policy.
- Computer Science Communities: Who is Speaking, and Who is Listening to the Women? Using an Ethics of Care to Promote Diverse Voices.
- 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.
- Documenting Computer Vision Datasets: An Invitation to Reflexive Data Practices.
- Leveraging Administrative Data for Bias Audits: Assessing Disparate Coverage with Mobility Data for COVID-19 Policy.
- Better Together?: How Externalities of Size Complicate Notions of Solidarity and Actuarial Fairness.
- Removing Spurious Features can Hurt Accuracy and Affect Groups Disproportionately.
- Evaluating Fairness of Machine Learning Models Under Uncertain and Incomplete Information.
- Data Leverage: A Framework for Empowering the Public in its Relationship with Technology Companies.
- The Use and Misuse of Counterfactuals in Ethical Machine Learning.
- Mitigating Bias in Set Selection with Noisy Protected Attributes.
- What We Can’t Measure, We Can’t Understand: Challenges to Demographic Data Procurement in the Pursuit of Fairness.
- Standardized Tests and Affirmative Action: The Role of Bias and Variance.
- The Sanction of Authority: Promoting Public Trust in AI.
- Algorithmic Fairness in Predicting Opioid Use Disorder using Machine Learning.
- Avoiding Disparity Amplification under Different Worldviews.
- Spoken Corpora Data, Automatic Speech Recognition, and Bias Against African American Language: The case of Habitual ‘Be’.
- Leave-one-out Unfairness.
- Fairness, Welfare, and Equity in Personalized Pricing.
- Re-imagining Algorithmic Fairness in India and Beyond.
- Narratives and Counternarratives on Data Sharing in Africa.
- This Whole Thing Smacks of Gender: Algorithmic Exclusion in Bioimpedance-based Body Composition Analysis.
- Algorithmic Recourse: from Counterfactual Explanations to Interventions.
- A Semiotics-based epistemic tool to reason about ethical issues in digital technology design and development.
- Measurement and Fairness.
- Fairness in Risk Assessment Instruments: Post-Processing to Achieve Counterfactual Equalized Odds.
- High Dimensional Model Explanations: An Axiomatic Approach.
- An Agent-based Model to Evaluate Interventions on Online Dating Platforms to Decrease Racial Homogamy.
- Designing Accountable Systems.
- 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.
- You Can’t Sit With Us: Exclusionary Pedagogy in AI Ethics Education.
- Fair Classification with Group-Dependent Label Noise.
- Censorship of Online Encyclopedias: Implications for NLP Models.
- Impossible Explanations?: Beyond explainable AI in the GDPR from a COVID-19 use case scenario.
- Towards Accountability for Machine Learning Datasets: Practices from Software Engineering and Infrastructure.
- Fairness, Equality, and Power in Algorithmic Decision-Making.
- One Label, One Billion Faces: Usage and Consistency of Racial Categories in Computer Vision.
- Reviewable Automated Decision-Making: A Framework for Accountable Algorithmic Systems.
- On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?
- Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AI.
- TILT: A GDPR-Aligned Transparency Information Language and Toolkit for Practical Privacy Engineering.
- From Papers to Programs: Courts, Corporations, Clinics and the Battle over Computerized Psychological Testing.
- A Statistical Test for Probabilistic Fairness.
- Building and Auditing Fair Algorithms: A Case Study in Candidate Screening.
- The Effect of the Rooney Rule on Implicit Bias in the Long Term.
- I agree with the decision, but they didn’t deserve this: Future Developers’ Perception of Fairness in Algorithmic Decisions.
- Image Representations Learned With Unsupervised Pre-Training Contain Human-like Biases.
- From Optimizing Engagement to Measuring Value.
- Chasing Your Long Tails: Differentially Private Prediction in Health Care Settings.
- Algorithmic Impact Assessments and Accountability: The Co-construction of Impacts.
- On the Moral Justification of Statistical Parity.
- Outlining Traceability: A Principle for Operationalizing Accountability in Computing Systems.
- An Action-Oriented AI Policy Toolkit for Technology Audits by Community Advocates and Activists.
- The Ethics of Emotion in Artificial Intelligence Systems.
- Detecting discriminatory risk through data annotation based on Bayesian inferences.
- How can I choose an explainer?: An Application-grounded Evaluation of Post-hoc Explanations.
- The Algorithmic Leviathan: Arbitrariness, Fairness, and Opportunity in Algorithmic Decision Making Systems.
- Epistemic values in feature importance methods: Lessons from feminist epistemology.
- A Bayesian Model of Cash Bail Decisions.
- The effect of differential victim crime reporting on predictive policing systems.
- Value Cards: An Educational Toolkit for Teaching Social Impacts of Machine Learning through Deliberation.
- BOLD: Dataset and Metrics for Measuring Biases in Open-Ended Language Generation.
- 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.
- Fair Graph Auto-Encoder for Unbiased Graph Representations with Wasserstein Distance.
- 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.
- On Statistical Bias In Active Learning: How and When to Fix It.
- 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.
- Debiasing Concept-based Explanations with Causal Analysis.
- Unbiased Teacher for Semi-Supervised Object Detection.
- Rethinking Soft Labels for Knowledge Distillation: A Bias-Variance Tradeoff Perspective.
- Direction Matters: On the Implicit Bias of Stochastic Gradient Descent with Moderate Learning Rate.
- A unifying view on implicit bias in training linear neural networks.
- What Makes Instance Discrimination Good for Transfer Learning?
- Clustering-friendly Representation Learning via Instance Discrimination and Feature Decorrelation.
- Shape-Texture Debiased Neural Network Training.
- The inductive bias of ReLU networks on orthogonally separable data.
- Statistical inference for individual fairness.
- What they do when in doubt: a study of inductive biases in seq2seq learners.
- Learning from others’ mistakes: Avoiding dataset biases without modeling them.
- Predicting Inductive Biases of Pre-Trained Models.
- Does enhanced shape bias improve neural network robustness to common corruptions?
- On Dyadic Fairness: Exploring and Mitigating Bias in Graph Connections.
- Why resampling outperforms reweighting for correcting sampling bias with stochastic gradients.
- Towards Resolving the Implicit Bias of Gradient Descent for Matrix Factorization: Greedy Low-Rank Learning.
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.
- Towards Reducing Biases in Combining Multiple Experts Online.
- Understanding the Effect of Bias in Deep Anomaly Detection.
- Graph Debiased Contrastive Learning with Joint Representation Clustering.
- 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
TBD
SDM 2021
- Fairness-aware Agnostic Federated Learning.
- Equitable Allocation of Healthcare Resources with Fair Survival Models.
- Fair Classification Under Strict Unawareness.
UAI 2021
TBD
WWW 2021
TBD
Others 2021
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.
- Combating Selection Biases in Recommender Systems with a Few Unbiased Ratings.
- 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
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
- Stretching the Effectiveness of MLE from Accuracy to Bias for Pairwise Comparisons.
- Learning Fair Representations for Kernel Models.
- Fair Decisions Despite Imperfect Predictions.
- Identifying and Correcting Label Bias in Machine Learning.
- 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
TBD
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
- What to account for when accounting for algorithms: a systematic literature review on algorithmic accountability.
- Algorithmic realism: expanding the boundaries of algorithmic thought.
- Algorithmic accountability in public administration: the GDPR paradox.
- 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.
- Explainability fact sheets: a framework for systematic assessment of explainable approaches.
- Multi-layered explanations from algorithmic impact assessments in the GDPR.
- The hidden assumptions behind counterfactual explanations and principal reasons.
- Why does my model fail?: contrastive local explanations for retail forecasting.
- “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.
- POTs: protective optimization technologies.
- Fair decision making using privacy-protected data.
- Fairness warnings and fair-MAML: learning fairly with minimal data.
- From ethics washing to ethics bashing: a view on tech ethics from within moral philosophy.
- Onward for the freedom of others: marching beyond the AI ethics.
- Whose side are ethics codes on?: power, responsibility and the social good.
- Algorithmic targeting of social policies: fairness, accuracy, and distributed governance.
- Roles for computing in social change.
- Regulating transparency?: Facebook, Twitter and the german network enforcement act.
- The relationship between trust in AI and trustworthy machine learning technologies.
- The philosophical basis of algorithmic recourse.
- Value-laden disciplinary shifts in machine learning.
- Effect of confidence and explanation on accuracy and trust calibration in AI-assisted decision making.
- Lessons from archives: strategies for collecting sociocultural data in machine learning.
- Data in New Delhi’s predictive policing system.
- Garbage in, garbage out?: do machine learning application papers in social computing report where human-labeled training data comes from?
- 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.
- Artificial mental phenomena: psychophysics as a framework to detect perception biases in AI models.
- The social lives of generative adversarial networks.
- Towards a more representative politics in the ethics of computer science.
- Integrating FATE/critical data studies into data science curricula: where are we going and how do we get there?
- 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.
- What’s sex got to do with machine learning?
- 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.
- Explaining machine learning classifiers through diverse counterfactual explanations.
- Model agnostic interpretability of rankers via intent modelling.
- Doctor XAI: an ontology-based approach to black-box sequential data classification explanations.
- Robustness in machine learning explanations: does it matter?
- Explainable machine learning in deployment.
- Fairness and utilization in allocating resources with uncertain demand.
- The effects of competition and regulation on error inequality in data-driven markets.
ICDM 2020
TBD
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.
- Fair k-Centers via Maximum Matching.
- 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.
- Learning De-biased Representations with Biased Representations.
- DeBayes: a Bayesian Method for Debiasing Network Embeddings.
- Data preprocessing to mitigate bias: A maximum entropy based approach.
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.
- Learning from Failure: De-biasing Classifier from Biased Classifier.
- 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
ASONAM 2020
- Bias in Knowledge Graph Embeddings.
- Debiasing Graph Representations via Metadata-Orthogonal Training.
2019
AAAI 2019
- Learning Optimal and Fair Decision Trees for Non-Discriminative Decision-Making
- 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
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
FAT* 2019
- Explaining Explanations in AI
- Deep Weighted Averaging Classifiers
- 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
- Beyond Open vs. Closed: Balancing Individual Privacy and Public Accountability in Data Sharing
- 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
- On Human Predictions with Explanations and Predictions of Machine Learning Models: A Case Study on Deception Detection
- Actionable Recourse in Linear Classification
- A Taxonomy of Ethical Tensions in Inferring Mental Health States from Social Media
- The Disparate Effects of Strategic Manipulation
- An Algorithmic Framework to Control Polarization in Personalization
- Racial categories in machine learning
- Downstream Effects of Affirmative Action
- Fairness through Causal Awareness: Learning Causal Latent-Variable Models for Biased Data
- Model Reconstruction from Model Explanations
- Fair Allocation through Competitive Equilibrium from Generic Incomes
- 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
- Robot Eyes Wide Shut: Understanding Dishonest Anthropomorphism
- 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
- The Profiling Potential of Computer Vision and the Challenge of Computational Empiricism
- Clear Sanctions, Vague Rewards: How China’s Social Credit System Defines “Good” and “Bad” Behavior
- Bias in Bios: A Case Study of Semantic Representation Bias in a High-Stakes Setting
- Who’s the Guinea Pig? Investigating Online A/B/n Tests In-The-Wild
- Fair Algorithms for Learning in Allocation Problems
- On Microtargeting Socially Divisive Ads: A Case Study of Russia-Linked Ad Campaigns on Facebook
- Model Cards for Model Reporting
- Dissecting Racial Bias in an Algorithm that Guides Health Decisions for 70 million people
- The Social Cost of Strategic Classification
- 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
- Characterizing Bias in Classifiers using Generative Models
- 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
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
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
- “Meaningful Information” and the Right to Explanation
- Interpretable Active Learning
- 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
- Mixed Messages? The Limits of Automated Social Media Content Analysis
- The cost of fairness in binary classification
- Decoupled Classifiers for Group-Fair and Efficient Machine Learning
- A case study of algorithm-assisted decision making in child maltreatment hotline screening decisions
- Fairness in Machine Learning: Lessons from Political Philosophy
- Runaway Feedback Loops in Predictive Policing
- 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
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
- Delayed Impact of Fair Machine Learning, Best Paper Awards
- 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
BIGDATA 2017
CIKM 2017
- FA*IR: A Fair Top-k Ranking Algorithm
- Algorithmic Bias: Do Good Systems Make Relevant Documents More Retrievable?
FAT* 2017
null
ICDM 2017
null
ICML 2017
IJCAI 2017
KDD 2017
NIPS 2017
- From Parity to Preference-based Notions of Fairness in Classification
- Controllable Invariance through Adversarial Feature Learning
- Avoiding Discrimination through Causal Reasoning
- Recycling Privileged Learning and Distribution Matching for Fairness
- 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
- Fair Optimal Stopping Policy for Matching with Mediator, supplement
- 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
null
AISTATS 2016
null
BIGDATA 2016
null
CIKM 2016
null
FAT* 2016
null
ICDM 2016
ICML 2016
null
IJCAI 2016
KDD 2016
null
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
UAI 2016
null
WWW 2016
null
Others 2016
- A KDD Process for Discrimination Discovery, ECML/PKDD 2016
2015
AAAI 2015
null
AISTATS 2015
null
BIGDATA 2015
null
CIKM 2015
null
FAT* 2015
null
ICDM 2015
null
ICML 2015
null
IJCAI 2015
null
KDD 2015
NIPS 2015
null
SDM 2015
null
UAI 2015
null
WWW 2015
null
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