The Neutrality Fallacy: When Algorithmic Fairness Interventions are (Not) Positive Action

Hilde Weerts (1) , Raphaële Xenidis (2) , Fabien Tarissan (3) , Henrik Palmer Olsen (4, 5) , Mykola Pechenizkiy (1)

1 TU/e - Eindhoven University of Technology [Eindhoven]
2 EdD - École de Droit de Sciences Po (Sciences Po)
3 ENS Paris Saclay - Ecole Normale Supérieure Paris-Saclay
4 UCPH - University of Copenhagen = Københavns Universitet
5 IEA de Paris - Institut d'études avancées de Paris

Various metrics and interventions have been developed to identify and mitigate unfair outputs of machine learning systems. While individuals and organizations have an obligation to avoid discrimination, the use of fairness-aware machine learning interventions has also been described as amounting to ‘algorithmic positive action’ under European Union (EU) non-discrimination law. As the Court of Justice of the European Union has been strict when it comes to assessing the lawfulness of positive action, this would impose a significant legal burden on those wishing to implement fair-ml interventions. In this paper, we propose that algorithmic fairness interventions often should be interpreted as a means to prevent discrimination, rather than a measure of positive action. Specifically, we suggest that this category mistake can often be attributed to neutrality fallacies: faulty assumptions regarding the neutrality of (fairness-aware) algorithmic decision-making. Our findings raise the question of whether a negative obligation to refrain from discrimination is sufficient in the context of algorithmic decision-making. Consequently, we suggest moving away from a duty to ‘not do harm’ towards a positive obligation to actively ‘do no harm’ as a more adequate framework for algorithmic decisionmaking and fair ml-interventions.

Mots clés: • Social and professional topics; Computing; technology policy Computing / technology policy • Computing methodologies → Artificial intelligence Machine learning • Applied computing → Law algorithmic decision-making; discrimination; positive action; EU law; Social and professional topics; Computing / technology policy; Artificial intelligence; Machine learning; Applied computing; Law algorithmic decision-making

Domaines: Droit

Hilde Weerts, Raphaële Xenidis, Fabien Tarissan, Henrik Palmer Olsen, Mykola Pechenizkiy. The Neutrality Fallacy: When Algorithmic Fairness Interventions are (Not) Positive Action. ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT), Jun 2024, Rio de Janeiro, Brazil. pp.11, ⟨10.1145/3630106.3659025⟩. ⟨hal-04565960⟩