Operationalizing Explainable Artificial Intelligence in the European Union Regulatory Ecosystem

Abstract

The European Union’s regulatory ecosystem presents challenges balancing legal and sociotechnical drivers for explainable AI systems. Core tensions emerge on dimensions of oversight, user needs and litigation. This paper maps provisions on algorithmic transparency and explainability across major EU data, AI, and platform policies using qualitative analysis. We characterize involved stakeholders and organizational implementation targets. Constraints become visible between useful transparency for accountability versus confidentiality protections. Through an AI hiring system example, we explore complications operationalizing explainability. Customization is required satisfying explainability desires within confidentiality and proportionality bounds. Findings advise technologists on prudent eXplainable AI technique selection given multi-dimensional tensions. Outcomes recommend policymakers balance worthy transparency goals with cohesive legislation enabling equitable dispute resolution.

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