On the Need and Applicability of Causality for Fairness: A Unified Framework for AI Auditing and Legal Analysis
This work addresses algorithmic discrimination for legal and societal stakeholders, but it is incremental as it reviews existing frameworks and proposes enhancements without introducing new methods.
The paper tackles the problem of evaluating fairness in AI systems by emphasizing the need for causal reasoning, highlighting challenges in proving causal claims due to opaque AI processes and proposing solutions to improve transparency and accountability.
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.