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.

翻译：基于机器学习的人工智能预测提供高质量解释是一项具有挑战性的复杂任务。其成功实施需要满足多项条件，包括：选择恰当的解释概括/具体层级；考虑解释受益者对相关人工智能任务的熟悉程度；引用促成决策的具体要素；利用可能未参与预测过程的额外知识（如专家证据）；以及提供支持反假设的证据。最终，系统需要以清晰可解释且具说服力的方式构建解释。基于上述考量，ANTIDOTE项目提出可解释人工智能的整合性愿景，将深度学习过程的底层特征与人类论证能力特有的高层模式相结合。该项目将利用深度学习和论证领域的跨学科能力，推动可解释人工智能更广泛、创新的视角发展——在临床病例决策场景中对高质量解释的需求尤为关键。作为项目首个成果，我们发布了Antidote CasiMedicos数据集，以期促进可解释人工智能的通用研究，特别是医学领域的论证应用。