Anti-Discrimination Learning: from Association to Causation - KDD 2018 Tutorial

08/19/2018

TL;DR

This tutorial introduces the causal modeling background, presents a causal modeling-based anti-discrimination framework, and covers the very latest research on causal modeling-based anti-discrimination learning.

Slides, Google Scholar

Abstract

Anti-discrimination learning is an increasingly important task in data mining and machine learning fields. Discrimination discovery is the problem of unveiling discriminatory practices by analyzing a dataset of historical decision records, and discrimination prevention aims to remove discrimination by modifying the biased data and/or the predictive algorithms. Discrimination is causal, which means that to prove discrimination one needs to derive a causal relationship rather than an association relationship. Although it is well-known that association does not mean causation, the gap between association and causation is not paid enough attention by many researchers. This tutorial first surveys existing association-based approaches and point out their limitations. Then, the tutorial introduces the causal modeling background, presents a causal modeling-based anti-discrimination framework, and covers the very latest research on causal modeling-based anti-discrimination learning. Finally, the tutorial suggests several potential future research directions.

Tutors

  • Dr. Lu Zhang is an assistant professor in the Computer Science and Computer Engineering Department, University of Arkansas. He received the BEng degree in computer science and engineering from the University of Science and Technology of China, in 2008, and the PhD degree in computer science from Nanyang Technological University, Singapore in 2013. He worked as a postdoctoral researcher at University of Arkansas during 2015 to 2018. His research interests include data mining algorithms, discrimination-aware data mining, and causal inference.
  • Yongkai Wu is a Ph.D. student in the Department of Computer Science and Computer Engineering at the University of Arkansas.
  • Dr. Xintao Wu is a Professor in the Department of Computer Science and Computer Engineering at the University of Arkansas. His major research interests include data privacy, bioinformatics and discrimination-aware data mining.

Target Audience, Prerequisite and Goal

The tutorial targets the researchers and practitioners who are interested in the issue of discovering and preventing discrimination caused by data mining and machine learning algorithms from the causal perspective. The audience is assumed to be familiar with the fundamental concepts in data mining and machine learning such as classification and predictive model training/testing.

The goal of this tutorial is to survey existing association-based approaches and point out their limitations, and introduce a causal modeling-based framework along with the very latest research on causal modeling-based anti-discrimination learning. By attending the tutorial, the attendees are expected to get acquainted with the anti-discrimination learning literature, have a deeper understanding of discrimination in machine learning and data mining from the causal perspective, and be enlighten about the future research directions in this field.

Outline

  • Introduction
  • Context
  • Literature and resource
  • Correlation based Anti-Discrimination Learning
  • Measuring discrimination:
    • fairness through unawareness,
    • disparate impact, individual fairness,
    • statistical parity,
    • equality of opportunity,
    • calibration,
    • conditional discrimination,
    • alpha discrimination,
    • multi-factor interaction,
    • belift,
    • preference.
  • Removing discrimination:
    • pre-processing (data modification, fair data representation, fair data generaiton),
    • in-processing (regularization, explicit constraints),
    • post-processing.
  • From correlation to causation: discrimination as causal effect, literature, motivating examples
  • Causal modeling background
  • From statistics to causal modeling
  • Structural causal model and causal graph: Markovian model, conditional independence, d-separation, factorization formula
  • Causal inference: intervention and do-operator, truncated factorization formula, path-specific effect, identifiability and "kite" structure, counterfactual analysis
  • Causal modeling-based anti-discrimination learning
  • Direct and indirect discrimination
  • Counterfactual fairness
  • Data discrimination vs. model discrimination
  • Other works on causal modeling-based anti-discrimination learning
  • Challenges and directions for future research
  • Summary of existing works
  • Challenges: non-identifiablity of path-specific effects, causal modeling for mixed-type variables, Semi-Markovian and ADMG, uncertain causal models, group/individual-level indirect discrimination
  • Future directions: building discrimination-free predictors, discrimination in tasks beyond classification: ranking and recommendation, Generative Adversarial Network (GAN), dynamic data and time series, text and image

Related Tutorials

  • Lu Zhang, Yongkai Wu and Xintao Wu. Anti-discrimination learning: From association to causation. Tutorial of IEEE BigData, Boston, MA, 2017.
  • Lu Zhang, Yongkai Wu and Xintao Wu. Anti-discrimination learning: From association to causation. Tutorial of SBP-BRiMS, Washington D.C., 2017.

Difference: In the tutorial we include the latest research on anti-discrimination learning published in the past half year, and adapt the tutorial to new topics in the area such as fairness via adversarial learning.

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