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.

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 given enough attention by many researchers. This tutorial first surveys existing association-based approaches and points 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.

Outline

• Introduction
  • Context
  • Literature and resources
• 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,
    • belief,
    • preference.
  • Removing discrimination:
    • pre-processing (data modification, fair data representation, fair data generation),
    • 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-identifiability 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