Contesting the algorithm: advancing a right to challenge AI decisions under the GDPR for algorithmic fairness
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Notice bibliographique
Résumé
Purpose This study aims to challenge the adequacy of Article 22 of the General Data Protection Regulation (GDPR) in safeguarding individuals against harmful automated decisions. It argues that explainability alone is insufficient for algorithmic accountability and proposes a legally enforceable right to contest such decisions. Through comparative legal analysis, it reveals the shortcomings of current GDPR protections and advocates for an amended framework that empowers individuals with substantive rights and remedies. The goal is to enable individuals not only to understand but also to challenge, correct or overturn artificial intelligence (AI)-driven decisions that significantly affect their lives. Design/methodology/approach This study adopts a qualitative comparative case study approach, analyzing enforcement and contestability in eight jurisdictions: four under the GDPR (The Netherlands, UK, France and Germany) and four outside it (California, New York City and Canada – public and private sectors). Data sources include legal texts, academic literature, court rulings and policy documents. A structured analytical matrix was applied to assess algorithm type, sector, availability of contestability mechanisms and enforcement effectiveness. This desk-based comparative legal analysis triangulates secondary sources to identify regulatory gaps and formulate reform proposals for strengthening contestability rights in AI governance. Findings The analysis reveals that GDPR Article 22 is functionally weak due to vague language, broad exceptions and limited enforcement. In practice, individuals rarely access meaningful mechanisms to contest consequential AI-driven decisions. By contrast, non-GDPR jurisdictions such as California and Canada show more proactive governance through opt-out rights, bias audits and algorithmic impact assessments. This study finds that effective contestability requires not only individual rights but also institutional safeguards, including human-in-the-loop review, independent oversight and public accountability mechanisms. Transparency alone is insufficient – robust, enforceable procedural rights are essential to ensure fairness and protect affected individuals. Originality/value This paper offers a novel ethical and legal case for rethinking algorithmic fairness beyond explainability, introducing a structured proposal to amend GDPR Article 22. It moves the discourse from transparency to contestability, grounded in comparative case analysis across EU and non-EU jurisdictions. The work bridges theoretical critique and practical reform, offering actionable policy recommendations, including an explicit right to contest, standards for human review and regulatory oversight models. It contributes original insights into how algorithmic harms can be addressed through due process-based contestation rights, reinforcing autonomy, fairness and justice in AI governance.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle