论文标题
自动决策系统中的公平性和解释性。计算机科学和法律的挑战
Fairness and Explainability in Automatic Decision-Making Systems. A challenge for computer science and law
论文作者
论文摘要
本文为分析自动算法决策中的公平问题的跨学科结构提供了贡献。第1节表明，监督学习中的技术选择具有需要考虑的社会影响。第2节提出了一种解决意外歧视问题的上下文方法，即面部中立的决策规则，但在社会群体之间产生不成比例的影响（例如，性别，种族或种族）。情境化将一方面关注美国的法律制度，另一方面侧重于欧洲。特别是，立法和判例法倾向于促进大西洋两岸的公平标准。第3节致力于算法决定的解释性；它将与技术概念（在欧洲和法国法律中）进行交叉引用法律概念，并将强调与算法决策有关的欧洲和法律法律文本的多数，甚至是多义的。结论提出了进一步研究的指示。
The paper offers a contribution to the interdisciplinary constructs of analyzing fairness issues in automatic algorithmic decisions. Section 1 shows that technical choices in supervised learning have social implications that need to be considered. Section 2 proposes a contextual approach to the issue of unintended group discrimination, i.e. decision rules that are facially neutral but generate disproportionate impacts across social groups (e.g., gender, race or ethnicity). The contextualization will focus on the legal systems of the United States on the one hand and Europe on the other. In particular, legislation and case law tend to promote different standards of fairness on both sides of the Atlantic. Section 3 is devoted to the explainability of algorithmic decisions; it will confront and attempt to cross-reference legal concepts (in European and French law) with technical concepts and will highlight the plurality, even polysemy, of European and French legal texts relating to the explicability of algorithmic decisions. The conclusion proposes directions for further research.
