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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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  • Why Human Prejudice is so Persistent: A Predictive Coding Analysis.Tzu-Wei Hung - 2023 - Social Epistemology 37 (6):779-797.
    Although the relationship between prejudice and predictive coding has attracted more attention recently, many important issues remain to be investigated, such as why prejudice is so persistent and how to accommodate seemingly conflicting studies. In this paper, we offer an integrated framework to explain the functional-computational mechanism of prejudice. We argue that this framework better explains (i) why prejudice is somewhat immune to revision, (ii) how inconsistent processing (e.g. one’s moral belief and biased emotional reaction) may occur, (iii) the dispute (...)
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  • Achieving Equity with Predictive Policing Algorithms: A Social Safety Net Perspective.Chun-Ping Yen & Tzu-Wei Hung - 2021 - Science and Engineering Ethics 27 (3):1-16.
    Whereas using artificial intelligence (AI) to predict natural hazards is promising, applying a predictive policing algorithm (PPA) to predict human threats to others continues to be debated. Whereas PPAs were reported to be initially successful in Germany and Japan, the killing of Black Americans by police in the US has sparked a call to dismantle AI in law enforcement. However, although PPAs may statistically associate suspects with economically disadvantaged classes and ethnic minorities, the targeted groups they aim to protect are (...)
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  • Engineering Equity: How AI Can Help Reduce the Harm of Implicit Bias.Ying-Tung Lin, Tzu-Wei Hung & Linus Ta-Lun Huang - 2020 - Philosophy and Technology 34 (S1):65-90.
    This paper focuses on the potential of “equitech”—AI technology that improves equity. Recently, interventions have been developed to reduce the harm of implicit bias, the automatic form of stereotype or prejudice that contributes to injustice. However, these interventions—some of which are assisted by AI-related technology—have significant limitations, including unintended negative consequences and general inefficacy. To overcome these limitations, we propose a two-dimensional framework to assess current AI-assisted interventions and explore promising new ones. We begin by using the case of human (...)
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