Procedural Fairness Through Decoupling Objectionable Data Generating Components (ICLR 2024 Spotlight)

This repository contains the implementation of the framework proposed in our paper "Procedural Fairness Through Decoupling Objectionable Data Generating Components".

Abstract

We reveal and address the frequently overlooked yet important issue of disguised procedural unfairness, namely, the potentially inadvertent alterations on the behavior of neutral (i.e., not problematic) aspects of data generating process, and/or the lack of procedural assurance of the greatest benefit of the least advantaged individuals. Inspired by John Rawls's advocacy for pure procedural justice, we view automated decision-making as a microcosm of social institutions, and consider how the data generating process itself can satisfy the requirements of procedural fairness. We propose a framework that decouples the objectionable data generating components from the neutral ones by utilizing reference points and the associated value instantiation rule. Our findings highlight the necessity of preventing disguised procedural unfairness, drawing attention not only to the objectionable data generating components that we aim to mitigate, but also more importantly, to the neutral components that we intend to keep unaffected.

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Running Environment Requirements

• python >= 3.9.6
• numpy >= 1.22.0
• scikit-learn >= 1.0.0
• torch >= 1.5.0

To Run the Code

Open uci_adult.ipynb in Jupyter Notebook, and click Run All to execute the notebook containing the implementation of our framework.