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  1. A Means-End Account of Explainable Artificial Intelligence.Oliver Buchholz - 2023 - Synthese 202 (33):1-23.
    Explainable artificial intelligence (XAI) seeks to produce explanations for those machine learning methods which are deemed opaque. However, there is considerable disagreement about what this means and how to achieve it. Authors disagree on what should be explained (topic), to whom something should be explained (stakeholder), how something should be explained (instrument), and why something should be explained (goal). In this paper, I employ insights from means-end epistemology to structure the field. According to means-end epistemology, different means ought to be (...)
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  • Freedom at Work: Understanding, Alienation, and the AI-Driven Workplace.Kate Vredenburgh - 2022 - Canadian Journal of Philosophy 52 (1):78-92.
    This paper explores a neglected normative dimension of algorithmic opacity in the workplace and the labor market. It argues that explanations of algorithms and algorithmic decisions are of noninstrumental value. That is because explanations of the structure and function of parts of the social world form the basis for reflective clarification of our practical orientation toward the institutions that play a central role in our life. Using this account of the noninstrumental value of explanations, the paper diagnoses distinctive normative defects (...)
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  • The Intriguing Relation Between Counterfactual Explanations and Adversarial Examples.Timo Freiesleben - 2021 - Minds and Machines 32 (1):1-33.
    The same method that creates adversarial examples to fool image-classifiers can be used to generate counterfactual explanations that explain algorithmic decisions. This observation has led researchers to consider CEs as AEs by another name. We argue that the relationship to the true label and the tolerance with respect to proximity are two properties that formally distinguish CEs and AEs. Based on these arguments, we introduce CEs, AEs, and related concepts mathematically in a common framework. Furthermore, we show connections between current (...)
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  • The Intriguing Relation Between Counterfactual Explanations and Adversarial Examples.Timo Freiesleben - 2021 - Minds and Machines 32 (1):77-109.
    The same method that creates adversarial examples to fool image-classifiers can be used to generate counterfactual explanations that explain algorithmic decisions. This observation has led researchers to consider CEs as AEs by another name. We argue that the relationship to the true label and the tolerance with respect to proximity are two properties that formally distinguish CEs and AEs. Based on these arguments, we introduce CEs, AEs, and related concepts mathematically in a common framework. Furthermore, we show connections between current (...)
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