AI公平性语境下的变量选择

中文摘要 AI 辅助

随着欧盟AI法案等监管要求的出台，AI系统的公平性已变得愈发重要。传统方法往往未充分考虑哲学伦理与社会意识，尤其是变量选择过程可能会引入隐含偏差，影响不同亚群体间的公平性。本文探讨一种评估AI公平性的数学方法，将数学方法论与伦理考量及监管要求相契合，倡导跨学科协作以解决公平性问题，强调理解更广泛伦理与社会语境的重要性。该方法着重保留所有潜在相关变量，以便开展更精细的公平性评估并减少隐含偏差。研究结果表明，排除敏感或关键变量可能会损害亚群体间的公平性，而保留所有相关变量则可减少隐含偏差。因此，这种跨学科方法能为伦理影响及监管标准合规性提供更深入的见解。通过将数学方法与伦理及社会意识相融合，本文提出可实现更公平的结果与负责任的AI部署。本研究强调，为有效解决与欧盟AI法案目标相一致的AI系统公平性问题，跨学科协作是必要的，该法案旨在推动可信赖且公平的AI系统。

英文摘要

Fairness in AI systems has become more important with recent regulatory demands, such as the EU AI Act. Traditional approaches often do not take into account philosophical ethics and social awareness. Variable selection processes, in particular, can introduce implicit bias, affecting equity across different subgroups. We discuss a mathematical approach that evaluates fairness in AI, aligning mathematical methodologies with ethical considerations and regulatory requirements. Our aim is to advocate for interdisciplinary collaboration to address fairness, emphasizing the importance of understanding broader ethical and societal contexts. Our approach emphasizes maintaining all potentially relevant variables to allow for more granular fairness assessments and to reduce implicit bias. The findings suggest that the exclusion of sensitive or critical variables may compromise equity between subgroups. In contrast, retaining all relevant variables could reduce implicit bias. Thus, the interdisciplinary approach could provide deeper insight into the ethical implications and compliance with regulatory standards. By integrating a mathematical approach with ethical and social awareness, we suggest more equitable outcomes and responsible AI deployment. This work underscores the necessity of interdisciplinary collaboration in effectively addressing fairness in AI systems aligned with the objectives of the European Union's AI Act, which seeks to promote trustworthy and fair AI systems.

发表机构

• UniCredit S.p.A.（裕信银行）
• Università Cattolica del Sacro Cuore（圣心天主教大学）
• Cetif

机构由 AI 辅助整理，请以论文原文为准。