🤖 AI Summary
This paper addresses the lack of causal accountability and discrimination risks arising from algorithmic opacity in AI-driven automated decision-making. Methodologically, it pioneers an interdisciplinary approach that systematically integrates EU case law, regulatory logic, causal graph models, counterfactual reasoning, and the Algorithmic Impact Assessment (AIA) framework—combining legal text analysis with algorithmic auditing. Its core contributions are threefold: (1) identifying a critical gap in causal explainability within current AI fairness audits; (2) constructing a unified evaluation framework tailored to judicial interpretability requirements; and (3) proposing actionable transparency-enhancement pathways to support regulatory compliance reviews and regulatory sandbox implementation. By bridging technical feasibility with legal accountability, this work establishes a methodological foundation and practical guidance for ensuring legally grounded, causally rigorous fairness assessment in AI governance.
📝 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.
