Contestable AI needs Computational Argumentation
Francesco Leofante, Hamed Ayoobi, Adam Dejl, Gabriel Freedman, Deniz Gorur, Junqi Jiang, Guilherme Paulino-Passos, Antonio Rago, Anna Rapberger, Fabrizio Russo, Xiang Yin, Dekai Zhang, Francesca Toni

TL;DR
This paper advocates for integrating computational argumentation into AI to enable dynamic explainability and contestability, aligning AI systems with regulatory and ethical standards.
Contribution
It proposes a framework where AI systems can interact, explain, and revise decisions through computational argumentation to achieve contestability.
Findings
Highlights the importance of dynamic explainability for contestable AI.
Suggests computational argumentation as a suitable approach for contestability.
Calls for a radical redesign of AI systems to incorporate contestability features.
Abstract
AI has become pervasive in recent years, but state-of-the-art approaches predominantly neglect the need for AI systems to be contestable. Instead, contestability is advocated by AI guidelines (e.g. by the OECD) and regulation of automated decision-making (e.g. GDPR). In this position paper we explore how contestability can be achieved computationally in and for AI. We argue that contestable AI requires dynamic (human-machine and/or machine-machine) explainability and decision-making processes, whereby machines can (i) interact with humans and/or other machines to progressively explain their outputs and/or their reasoning as well as assess grounds for contestation provided by these humans and/or other machines, and (ii) revise their decision-making processes to redress any issues successfully raised during contestation. Given that much of the current AI landscape is tailored to static…
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