Unequal Uncertainty: Rethinking Algorithmic Interventions for Mitigating Discrimination from AI

Holli Sargeant, Mackenzie Jorgensen, Arina Shah, Sam Goring, Adrian Weller, Umang Bhatt
Proceedings of Fifth European Conference on Algorithmic Fairness, PMLR 350:386-408, 2026.

Abstract

Uncertainty in artificial intelligence (AI) predictions raises pressing legal and ethical questions for AI-assisted decision-making. This article examines two uncertainty-based algorithmic interventions that act as guardrails for human-AI interaction: selective abstention, which withholds high-uncertainty predictions from human decision-makers, and selective friction, which presents such predictions together with salient warnings about the model’s uncertainty. Prior work suggests that uncertainty-based abstention can exacerbate disparities where under-represented groups are more likely to receive uncertain predictions. We provide, to our knowledge, the first doctrinal analysis of uncertainty-based algorithmic interventions under laws from the United Kingdom and examine their consequences through two AI-assisted case studies: consumer credit and risk of reoffending. We show that the use of uncertainty thresholds, though formally neutral, can generate discriminatory effects. We argue that both interventions pose risks of unlawful discrimination, but that selective friction is legally preferable. It preserves access to the prediction and is more likely to satisfy proportionality under the Equality Act 2010. Whether selective friction also improves decision quality in practice is uncertain. We identify conditions under which it may improve or worsen decision quality.

Cite this Paper

BibTeX
@InProceedings{pmlr-v350-sargeant26a, title = {Unequal Uncertainty: Rethinking Algorithmic Interventions for Mitigating Discrimination from AI}, author = {Sargeant, Holli and Jorgensen, Mackenzie and Shah, Arina and Goring, Sam and Weller, Adrian and Bhatt, Umang}, booktitle = {Proceedings of Fifth European Conference on Algorithmic Fairness}, pages = {386--408}, year = {2026}, editor = {De Bie, Tijl and Defrance, MaryBeth and Calders, Toon and Nguyen, Dennis}, volume = {350}, series = {Proceedings of Machine Learning Research}, month = {02--04 Sep}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v350/main/assets/sargeant26a/sargeant26a.pdf}, url = {https://proceedings.mlr.press/v350/sargeant26a.html}, abstract = {Uncertainty in artificial intelligence (AI) predictions raises pressing legal and ethical questions for AI-assisted decision-making. This article examines two uncertainty-based algorithmic interventions that act as guardrails for human-AI interaction: selective abstention, which withholds high-uncertainty predictions from human decision-makers, and selective friction, which presents such predictions together with salient warnings about the model’s uncertainty. Prior work suggests that uncertainty-based abstention can exacerbate disparities where under-represented groups are more likely to receive uncertain predictions. We provide, to our knowledge, the first doctrinal analysis of uncertainty-based algorithmic interventions under laws from the United Kingdom and examine their consequences through two AI-assisted case studies: consumer credit and risk of reoffending. We show that the use of uncertainty thresholds, though formally neutral, can generate discriminatory effects. We argue that both interventions pose risks of unlawful discrimination, but that selective friction is legally preferable. It preserves access to the prediction and is more likely to satisfy proportionality under the Equality Act 2010. Whether selective friction also improves decision quality in practice is uncertain. We identify conditions under which it may improve or worsen decision quality.} }
Endnote
%0 Conference Paper %T Unequal Uncertainty: Rethinking Algorithmic Interventions for Mitigating Discrimination from AI %A Holli Sargeant %A Mackenzie Jorgensen %A Arina Shah %A Sam Goring %A Adrian Weller %A Umang Bhatt %B Proceedings of Fifth European Conference on Algorithmic Fairness %C Proceedings of Machine Learning Research %D 2026 %E Tijl De Bie %E MaryBeth Defrance %E Toon Calders %E Dennis Nguyen %F pmlr-v350-sargeant26a %I PMLR %P 386--408 %U https://proceedings.mlr.press/v350/sargeant26a.html %V 350 %X Uncertainty in artificial intelligence (AI) predictions raises pressing legal and ethical questions for AI-assisted decision-making. This article examines two uncertainty-based algorithmic interventions that act as guardrails for human-AI interaction: selective abstention, which withholds high-uncertainty predictions from human decision-makers, and selective friction, which presents such predictions together with salient warnings about the model’s uncertainty. Prior work suggests that uncertainty-based abstention can exacerbate disparities where under-represented groups are more likely to receive uncertain predictions. We provide, to our knowledge, the first doctrinal analysis of uncertainty-based algorithmic interventions under laws from the United Kingdom and examine their consequences through two AI-assisted case studies: consumer credit and risk of reoffending. We show that the use of uncertainty thresholds, though formally neutral, can generate discriminatory effects. We argue that both interventions pose risks of unlawful discrimination, but that selective friction is legally preferable. It preserves access to the prediction and is more likely to satisfy proportionality under the Equality Act 2010. Whether selective friction also improves decision quality in practice is uncertain. We identify conditions under which it may improve or worsen decision quality.
APA
Sargeant, H., Jorgensen, M., Shah, A., Goring, S., Weller, A. & Bhatt, U.. (2026). Unequal Uncertainty: Rethinking Algorithmic Interventions for Mitigating Discrimination from AI. Proceedings of Fifth European Conference on Algorithmic Fairness, in Proceedings of Machine Learning Research 350:386-408 Available from https://proceedings.mlr.press/v350/sargeant26a.html.

Related Material