Article 10 of the EU’s AI Act puts data governance at the heart of bias mitigation in high-risk AI systems but offers little guidance on implementation. Delegating these challenges to technical standardisation bodies raises both feasibility and legitimacy concerns, posing a significant test for the institutions now tasked with defining AI fairness in practice.

Fairness as crowd-pleaser

Lee Andrew Bygrave, University of Oslo

PUBLISHED ON: 24 Jul 2025

Given the ubiquity of fairness as a normative criterion in tech policy, this op-ed warns of particular risks to its legitimising potential which may, in the long term, damage the standing of fairness as crowd-pleaser.

While transparency is often championed as the key to addressing the risks of automated decision-making (ADM) in public governance, this op-ed argues that a narrow focus on explainability overlooks deeper systemic issues such as power imbalances, commercial influence, and weakened accountability. To address these issues, mechanisms that promote transparency must operate alongside efforts to enhance citizen engagement and other methods of oversight and accountability to better protect democratic values.

This op-ed defends the Universal Inscrutability Argument by clarifying what legal explainability actually requires: justifying reasons for institutional decisions, not access to individual motivations. The argument holds that legal standards for explainability should be based on the latter, not the former.