On the Need and Applicability of Causality for Fairness: A Unified Framework for AI Auditing and Legal Analysis

TL;DR

This paper emphasizes the importance of causal reasoning in AI fairness, highlighting legal and societal challenges, and proposing solutions to improve transparency and accountability in algorithmic decisions.

Contribution

It provides a unified framework integrating causality for AI auditing and legal analysis, addressing practical challenges in applying causal inference for fairness.

Findings

Causal reasoning is crucial for assessing AI fairness.

Legal frameworks influence causal analysis in AI.

Proposed solutions enhance transparency and accountability.

Abstract

As Artificial Intelligence (AI) increasingly influences decisions in critical societal sectors, understanding and establishing causality becomes essential for evaluating the fairness of automated systems. This article explores the significance of causal reasoning in addressing algorithmic discrimination, emphasizing both legal and societal perspectives. By reviewing landmark cases and regulatory frameworks, particularly within the European Union, we illustrate the challenges inherent in proving causal claims when confronted with opaque AI decision-making processes. The discussion outlines practical obstacles and methodological limitations in applying causal inference to real-world fairness scenarios, proposing actionable solutions to enhance transparency, accountability, and fairness in algorithm-driven decisions.