Fairness and Explainability in Automatic Decision-Making Systems. A challenge for computer science and law
It addresses fairness and explainability issues in automated systems for computer science and law, but is incremental as it reviews and contextualizes existing concepts without introducing new technical methods.
The paper examines fairness and explainability in algorithmic decision-making, highlighting how technical choices in supervised learning have social implications and proposing a contextual approach to unintended group discrimination across U.S. and European legal systems.
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.