Causal Fair Machine Learning via Rank-Preserving Interventional Distributions

TL;DR

It is shown that the warping approach effectively identifies the most discriminated individuals and mitigates unfairness and is compared with a different approach for mitigating unfairness by causally preprocessing data that uses quantile regression forests.

Abstract

A decision can be defined as fair if equal individuals are treated equally and unequals unequally. Adopting this definition, the task of designing machine learning (ML) models that mitigate unfairness in automated decision-making systems must include causal thinking when introducing protected attributes: Following a recent proposal, we define individuals as being normatively equal if they are equal in a fictitious, normatively desired (FiND) world, where the protected attributes have no (direct or indirect) causal effect on the target. We propose rank-preserving interventional distributions to define a specific FiND world in which this holds and a warping method for estimation. Evaluation criteria for both the method and the resulting ML model are presented and validated through simulations. Experiments on empirical data showcase the practical application of our method and compare results with "fairadapt" (Plečko and Meinshausen, 2020), a different approach for mitigating unfairness by causally preprocessing data that uses quantile regression forests. With this, we show that our warping approach effectively identifies the most discriminated individuals and mitigates unfairness.

Figures (11)

• Figure 1: Assumed DAGs for credit risk example. In the FiND world, only solid arrows exist, and in the real world, all arrows exist.
• Figure 2: Rank-preserving interventional distribution: Transform female observation to the corresponding quantile in male distribution.
• Figure 3: Illustration of the three worlds: Real world discriminates between males and females, FiND world does not discriminate, warped world estimates FiND world by warping females to male level.
• Figure 4: Visualization of residual-based warping approach: Real-world Amount of female individual (* in left plot) is warped to the respective quantile of male distribution (* in right plot). Subscript A in $\pi^{f}_A(c^{(i)}), x_A^{(i)}$, etc. is omitted for better readability.
• Figure 5: Average distribution of Amount in different worlds.