论文标题
关于因果关系公平性的需求和适用性：AI审计和法律分析的统一框架
On the Need and Applicability of Causality for Fairness: A Unified Framework for AI Auditing and Legal Analysis
论文作者
论文摘要
随着人工智能（AI）日益影响关键社会部门的决策，理解和建立因果关系对于评估自动化系统的公平性至关重要。本文探讨了因果推理在解决算法歧视方面的重要性，强调法律和社会观点。通过审查具有里程碑意义的案件和监管框架，尤其是在欧盟内部，我们说明了在面对不透明的AI决策过程时证明因果主张所固有的挑战。讨论概述了将因果推断应用于现实世界公平场景的实际障碍和方法论上的局限性，提出了可行的解决方案，以提高算法驱动决策中的透明度，问责制和公平性。
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.
