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  1. Explainable Artificial Intelligence in Data Science.Joaquín Borrego-Díaz & Juan Galán-Páez - 2022 - Minds and Machines 32 (3):485-531.
    A widespread need to explain the behavior and outcomes of AI-based systems has emerged, due to their ubiquitous presence. Thus, providing renewed momentum to the relatively new research area of eXplainable AI (XAI). Nowadays, the importance of XAI lies in the fact that the increasing control transference to this kind of system for decision making -or, at least, its use for assisting executive stakeholders- already affects many sensitive realms (as in Politics, Social Sciences, or Law). The decision-making power handover to (...)
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  • G -LIME: Statistical learning for local interpretations of deep neural networks using global priors.Xuhong Li, Haoyi Xiong, Xingjian Li, Xiao Zhang, Ji Liu, Haiyan Jiang, Zeyu Chen & Dejing Dou - 2023 - Artificial Intelligence 314 (C):103823.
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  • Self-Legislating Machines: What Can Kant Teach Us About Original Intentionality?Richard Evans - 2022 - Kant Studien 113 (3):555-576.
    In this paper, I attempt to address a fundamental challenge for machine intelligence: to understand whether and how a machine’s internal states and external outputs can exhibit original non-derivative intentionality. This question has three aspects. First, what does it take for a machine to exhibit original de dicto intentionality? Second, what does it take to exhibit original de re intentionality? Third, what is required for the machine to defer to the external objective world by respecting the word-to-world direction of fit? (...)
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  • A differentiable first-order rule learner for inductive logic programming.Kun Gao, Katsumi Inoue, Yongzhi Cao & Hanpin Wang - 2024 - Artificial Intelligence 331 (C):104108.
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