EXPLAINABLE ARTIFICIAL INTELLIGENCE AND ALGORITHMIC FAIRNESS: ADDRESSING HUMAN RIGHTS CHALLENGES IN DATA SCIENCE APPLICATIONS
Keywords:
explainable AI, algorithmic fairness, jurisprudence, human rights, PDAA, GDPR, AI Act, non-delegation doctrine, counterfactual recourse, distributional shift.Abstract
Automated decision-making systems now govern access to employment, credit, healthcare, and criminal justice across dozens of jurisdictions. This article advances the thesis that algorithmic opacity and algorithmic discrimination are not two separate problems but expressions of a single structural failure: the Compounding Opacity Problem (COP), in which each successive layer of technical and legal accountability generates its own epistemic barrier, rendering oversight formally possible but substantively meaningless. We demonstrate that post-hoc explainability methods are legally inadequate, that fairness metrics are mathematically incompatible with one another, and that current regulatory frameworks lack the technical specificity to detect either failure. We further advance an original jurisprudential argument: the selection of a fairness metric is not a technical act but a normative one with the distributional consequences of primary legislation, and its current delegation to developers without statutory constraint constitutes an unconstitutional non-delegation of legislative authority. Against this diagnosis, we propose Pre-Deployment Adversarial Auditing (PDAA), an integrated framework combining adversarial pipeline probing, subgroup stress-testing under distributional shift, counterfactual recourse certification, and continuous post-deployment monitoring, supported by National Algorithmic Audit Authorities with independent technical access powers.
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