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? (...) I attempt to answer the first challenge by providing a constitutive counts-as understanding of de dicto intentionality. This analysis involves repurposing Kant’s vision of a self-legislating agent as a specification of a machine that reprograms itself. I attempt to answer the second and third challenges by extending Kant’s synchronic model of de dicto intentionality with Brandom’s interpretation of Hegel’s diachronic model of de re intentionality, using Hegel’s notion of recollection to provide an understanding of what is involved in achieving deference to the external world. (shrink)

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 (...) opaque AI systems makes mandatory explaining those, primarily in application scenarios where the stakeholders are unaware of both the high technology applied and the basic principles governing the technological solutions. The issue should not be reduced to a merely technical problem; the explainer would be compelled to transmit richer knowledge about the system (including its role within the informational ecosystem where he/she works). To achieve such an aim, the explainer could exploit, if necessary, practices from other scientific and humanistic areas. The first aim of the paper is to emphasize and justify the need for a multidisciplinary approach that is beneficiated from part of the scientific and philosophical corpus on Explaining, underscoring the particular nuances of the issue within the field of Data Science. The second objective is to develop some arguments justifying the authors’ bet by a more relevant role of ideas inspired by, on the one hand, formal techniques from Knowledge Representation and Reasoning, and on the other hand, the modeling of human reasoning when facing the explanation. This way, explaining modeling practices would seek a sound balance between the pure technical justification and the explainer-explainee agreement. (shrink)