Argumentative debates for transparent bias detection

Ayoobi, Hamed, Potyka, Nico, Rapberger, Anna and Toni, Francesca 2026. Argumentative debates for transparent bias detection. Presented at: The 40th Annual AAAI Conference on Artificial Intelligence (AAAI), Singapore, Republic of Singapore, 20-27January 2026. Item availability restricted.

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Abstract

As the use of AI in society grows, addressing emerging biases is essential to prevent systematic discrimination. Several bias detection methods have been proposed, but, with few exceptions, these tend to ignore transparency. Instead, interpretability and explainability are core requirements for algorithmic fairness, even more so than for other algorithmic solutions, given the human-oriented nature of fairness. We present ABIDE (Argumentative BIas detection by DEbate), a novel framework that structures bias detection transparently as debate, guided by an underlying argument graph as understood in (formal and computational) argumentation. The arguments are about the success chances of groups in local neighbourhoods and the significance of these neighbourhoods. We evaluate ABIDE experimentally and demonstrate its strengths in performance against an argumentative baseline.

Item Type:	Conference or Workshop Item - published (Paper)
Status:	In Press
Schools:	Schools > Computer Science & Informatics
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Date of First Compliant Deposit:	29 January 2026
Date of Acceptance:	15 November 2025
Last Modified:	29 Jan 2026 16:32
URI:	https://orca.cardiff.ac.uk/id/eprint/183889

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