Contestable AI needs Computational Argumentation
<|reference_start|>Contestable AI needs Computational Argumentation: 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.<|reference_end|>
@article{leofante2024contestable,
    title={Contestable AI needs Computational Argumentation},
    author={Francesco Leofante and Hamed Ayoobi and Adam Dejl and Gabriel Freedman
  and Deniz Gorur and Junqi Jiang and Guilherme Paulino-Passos and Antonio Rago
  and Anna Rapberger and Fabrizio Russo and Xiang Yin and Dekai Zhang and
  Francesca Toni},
    journal={arXiv preprint arXiv:2405.10729},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.10729},
    primaryClass={cs.AI}
}