Fairness by Design: An Empowering tool for Personal Data Processing in AI systems
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Fairness has long stood at the centre of European data-protection law, yet remains its most ambiguous and least implemented principle. In the age of AI—where automated models mediate access to knowledge, work, and opportunity—such ambiguity is untenable. This paper reclaims fairness as an empowerment mechanism rather than a procedural formality. Drawing on Article 5(1)(a) GDPR, the jurisprudence of the Court of Justice of the European Union, and guidance from the EDPB and EDPS, it develops a Fairness-by-Design framework that integrates procedural fairness—transparency, explainability, participation—with substantive fairness—accountability, contextual proportionality, and reciprocity. The framework positions fairness as a bridge between legality and legitimacy, transforming data subjects from passive recipients of rights into active participants in governance. Through interdisciplinary analysis combining legal doctrine, human–computer interaction, and AI ethics, the study demonstrates that participatory, contextual, and reciprocal fairness operationalise empowerment across the AI lifecycle. Fairness-by-Design is proposed as both a normative claim and a practical roadmap: a method for translating legal rights into design choices and embedding equity and agency within AI governance.
Keywords: Fairness; Data protection; GDPR; Artificial Intelligence; Participatory design; Empowerment
This paper has sought to reclaim fairness as the law’s most human principle: the standard that translates dignity into design and autonomy into architecture.
