LUCID-GAN: Conditional Generative Models to Locate Unfairness

TL;DR

LUCID-GAN introduces a conditional generative approach to identify and analyze unethical biases in black-box models by producing realistic, canonical inputs, overcoming limitations of gradient-based inverse design methods.

Contribution

It extends the LUCID framework by using a generative model to locate unfairness, applicable to non-differentiable models and capable of assessing complex discrimination.

Findings

Detects biases in black-box models without training data access

Generates realistic canonical inputs for bias analysis

Applicable to non-differentiable models

Abstract

Most group fairness notions detect unethical biases by computing statistical parity metrics on a model's output. However, this approach suffers from several shortcomings, such as philosophical disagreement, mutual incompatibility, and lack of interpretability. These shortcomings have spurred the research on complementary bias detection methods that offer additional transparency into the sources of discrimination and are agnostic towards an a priori decision on the definition of fairness and choice of protected features. A recent proposal in this direction is LUCID (Locating Unfairness through Canonical Inverse Design), where canonical sets are generated by performing gradient descent on the input space, revealing a model's desired input given a preferred output. This information about the model's mechanisms, i.e., which feature values are essential to obtain specific outputs, allows exposing potential unethical biases in its internal logic. Here, we present LUCID-GAN, which generates canonical inputs via a conditional generative model instead of gradient-based inverse design. LUCID-GAN has several benefits, including that it applies to non-differentiable models, ensures that canonical sets consist of realistic inputs, and allows to assess proxy and intersectional discrimination. We empirically evaluate LUCID-GAN on the UCI Adult and COMPAS data sets and show that it allows for detecting unethical biases in black-box models without requiring access to the training data.