Contestable AI needs Computational Argumentation
| Autor: | Leofante, Francesco, Ayoobi, Hamed, Dejl, Adam, Freedman, Gabriel, Gorur, Deniz, Jiang, Junqi, Paulino-Passos, Guilherme, Rago, Antonio, Rapberger, Anna, Russo, Fabrizio, Yin, Xiang, Zhang, Dekai, Toni, Francesca |
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| Rok vydání: | 2024 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| Popis: | 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. Comment: Accepted at KR 2024 |
| Databáze: | arXiv |
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