Contestable AI needs Computational Argumentation
- URL: http://arxiv.org/abs/2405.10729v1
- Date: Fri, 17 May 2024 12:23:18 GMT
- Title: Contestable AI needs Computational Argumentation
- Authors: 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,
- Abstract summary: State-of-the-art approaches predominantly neglect the need for AI systems to be contestable.
We argue that contestable AI requires dynamic (human-machine and/or machine-machine) explainability and decision-making processes.
- Score: 15.15970495693702
- License: http://creativecommons.org/licenses/by/4.0/
- 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 AIs, the need to accommodate contestability will require a radical rethinking, that, we argue, computational argumentation is ideally suited to support.
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