Unequal Uncertainty: Rethinking Algorithmic Interventions for Mitigating Discrimination from AI
This addresses the legal and ethical challenges of AI uncertainty for decision-making in domains like consumer credit and content moderation, though it is incremental as it builds on prior research on disparities.
The paper tackles the problem that algorithmic interventions based on uncertainty, such as selective abstention, can inadvertently exacerbate discrimination against underrepresented groups in AI-assisted decision-making, and finds that selective friction offers a fairer alternative by preserving transparency and encouraging cautious human judgment.
Uncertainty in artificial intelligence (AI) predictions poses urgent legal and ethical challenges for AI-assisted decision-making. We examine two algorithmic interventions that act as guardrails for human-AI collaboration: selective abstention, which withholds high-uncertainty predictions from human decision-makers, and selective friction, which delivers those predictions together with salient warnings or disclosures that slow the decision process. Research has shown that selective abstention based on uncertainty can inadvertently exacerbate disparities and disadvantage under-represented groups that disproportionately receive uncertain predictions. In this paper, we provide the first integrated socio-technical and legal analysis of uncertainty-based algorithmic interventions. Through two case studies, AI-assisted consumer credit decisions and AI-assisted content moderation, we demonstrate how the seemingly neutral use of uncertainty thresholds can trigger discriminatory impacts. We argue that, although both interventions pose risks of unlawful discrimination under UK law, selective frictions offer a promising pathway toward fairer and more accountable AI-assisted decision-making by preserving transparency and encouraging more cautious human judgment.