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  1. AI, Opacity, and Personal Autonomy.Bram Vaassen - 2022 - Philosophy and Technology 35 (4):1-20.
    Advancements in machine learning have fuelled the popularity of using AI decision algorithms in procedures such as bail hearings, medical diagnoses and recruitment. Academic articles, policy texts, and popularizing books alike warn that such algorithms tend to be opaque: they do not provide explanations for their outcomes. Building on a causal account of transparency and opacity as well as recent work on the value of causal explanation, I formulate a moral concern for opaque algorithms that is yet to receive a (...)
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  • Understanding, Idealization, and Explainable AI.Will Fleisher - forthcoming - Episteme:1-27.
    Many AI systems that make important decisions are black boxes: how they function is opaque even to their developers. This is due to their high complexity and to the fact that they are trained rather than programmed. Efforts to alleviate the opacity of black box systems are typically discussed in terms of transparency, interpretability, and explainability. However, there is little agreement about what these key concepts mean, which makes it difficult to adjudicate the success or promise of opacity alleviation methods. (...)
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  • Philosophy of Science at Sea: Clarifying the Interpretability of Machine Learning.Claus Beisbart & Tim Räz - 2022 - Philosophy Compass 17 (6):e12830.
    Philosophy Compass, Volume 17, Issue 6, June 2022.
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  • From Responsibility to Reason-Giving Explainable Artificial Intelligence.Kevin Baum, Susanne Mantel, Timo Speith & Eva Schmidt - 2022 - Philosophy and Technology 35 (1):1-30.
    We argue that explainable artificial intelligence (XAI), specifically reason-giving XAI, often constitutes the most suitable way of ensuring that someone can properly be held responsible for decisions that are based on the outputs of artificial intelligent (AI) systems. We first show that, to close moral responsibility gaps (Matthias 2004), often a human in the loop is needed who is directly responsible for particular AI-supported decisions. Second, we appeal to the epistemic condition on moral responsibility to argue that, in order to (...)
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