HiTZ@Antidote: Argumentation-driven Explainable Artificial Intelligence for Digital Medicine

• URL: http://arxiv.org/abs/2306.06029v1
• Date: Fri, 9 Jun 2023 16:50:02 GMT
• Title: HiTZ@Antidote: Argumentation-driven Explainable Artificial Intelligence for Digital Medicine
• Authors: Rodrigo Agerri, I\~nigo Alonso, Aitziber Atutxa, Ander Berrondo, Ainara Estarrona, Iker Garcia-Ferrero, Iakes Goenaga, Koldo Gojenola, Maite Oronoz, Igor Perez-Tejedor, German Rigau and Anar Yeginbergenova
• Abstract summary: ANTIDOTE fosters an integrated vision of explainable AI, where low-level characteristics of the deep learning process are combined with higher level schemes proper of the human argumentation capacity. As a first result of the project, we publish the Antidote CasiMedicos dataset to facilitate research on explainable AI in general, and argumentation in the medical domain in particular.
• Score: 7.089952396422835
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Providing high quality explanations for AI predictions based on machine learning is a challenging and complex task. To work well it requires, among other factors: selecting a proper level of generality/specificity of the explanation; considering assumptions about the familiarity of the explanation beneficiary with the AI task under consideration; referring to specific elements that have contributed to the decision; making use of additional knowledge (e.g. expert evidence) which might not be part of the prediction process; and providing evidence supporting negative hypothesis. Finally, the system needs to formulate the explanation in a clearly interpretable, and possibly convincing, way. Given these considerations, ANTIDOTE fosters an integrated vision of explainable AI, where low-level characteristics of the deep learning process are combined with higher level schemes proper of the human argumentation capacity. ANTIDOTE will exploit cross-disciplinary competences in deep learning and argumentation to support a broader and innovative view of explainable AI, where the need for high-quality explanations for clinical cases deliberation is critical. As a first result of the project, we publish the Antidote CasiMedicos dataset to facilitate research on explainable AI in general, and argumentation in the medical domain in particular.

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