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  1. What do we want from Explainable Artificial Intelligence (XAI)? – A stakeholder perspective on XAI and a conceptual model guiding interdisciplinary XAI research.Markus Langer, Daniel Oster, Timo Speith, Lena Kästner, Kevin Baum, Holger Hermanns, Eva Schmidt & Andreas Sesing - 2021 - Artificial Intelligence 296 (C):103473.
    Previous research in Explainable Artificial Intelligence (XAI) suggests that a main aim of explainability approaches is to satisfy specific interests, goals, expectations, needs, and demands regarding artificial systems (we call these “stakeholders' desiderata”) in a variety of contexts. However, the literature on XAI is vast, spreads out across multiple largely disconnected disciplines, and it often remains unclear how explainability approaches are supposed to achieve the goal of satisfying stakeholders' desiderata. This paper discusses the main classes of stakeholders calling for explainability (...)
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  2. Where Reasons and Reasoning Come Apart.Eva Schmidt - 2020 - Noûs 55 (4):762-781.
    Proponents of the reasoning view analyze normative reasons as premises of good reasoning and explain the normativity of reasons by appeal to their role as premises of good reasoning. The aim of this paper is to cast doubt on the reasoning view by providing counterexamples to the proposed analysis of reasons, counterexamples in which premises of good reasoning towards φ‐ing are not reasons to φ.
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  3. Possessing epistemic reasons: the role of rational capacities.Eva Https:https://Orcidorg Schmidt - 2019 - Philosophical Studies 176 (2):483-501.
    In this paper, I defend a reasons-first view of epistemic justification, according to which the justification of our beliefs arises entirely in virtue of the epistemic reasons we possess. I remove three obstacles for this view, which result from its presupposition that epistemic reasons have to be possessed by the subject: the problem that reasons-first accounts of justification are necessarily circular; the problem that they cannot give special epistemic significance to perceptual experience; the problem that they have to say that (...)
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  4. The Explanatory Merits of Reasons-First Epistemology.Eva Schmidt - 2020 - In Christoph Demmerling & Dirk Schröder (eds.), Concepts in Thought, Action, and Emotion: New Essays. New York, NY: Routledge. pp. 75-91.
    I present an explanatory argument for the reasons-first view: It is superior to knowledge-first views in particular in that it can both explain the specific epistemic role of perception and account for the shape and extent of epistemic justification.
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  5. Hume and the Unity of Reasons.Eva Schmidt - 2024 - In Scott Stapleford & Verena Wagner (eds.), Hume and contemporary epistemology. New York, NY: Routledge.
    Current debates about reasons and reasoning often draw comparisons between epistemic and practical reasons and reasoning and presuppose substantial unity between the practical and epistemic domains. This stance seems to conflict with a stark Humean contrast between the two domains: With respect to practical reasons and reasoning, Hume highlights the role of impressions, especially the passions, in motivating and rationalizing action, while apparently downplaying the potential relevance of beliefs, reason, or reasons. With respect to epistemic reasons and theoretical reasoning, he (...)
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  6. How to Make Norms Clash.Eva Schmidt - 2021 - Australasian Philosophical Review 5 (1):46-55.
    In this comment on Katherine Dormandy's paper «True Faith», I point out that the clash she describes between epistemic norms and faith-based norms of belief needs to be supplemented with a clear understanding of the pertinent norms of belief. I argue that conceiving of them as evaluative fails to explain the clash, and that understanding them as prescriptive is no better. I suggest an understanding of these norms along the lines of Ross’s (1930) prima facie duties, and show how this (...)
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  7. Two challenges for CI trustworthiness and how to address them.Kevin Baum, Eva Schmidt & A. Köhl Maximilian - 2017
    We argue that, to be trustworthy, Computa- tional Intelligence (CI) has to do what it is entrusted to do for permissible reasons and to be able to give rationalizing explanations of its behavior which are accurate and gras- pable. We support this claim by drawing par- allels with trustworthy human persons, and we show what difference this makes in a hypo- thetical CI hiring system. Finally, we point out two challenges for trustworthy CI and sketch a mechanism which could be (...)
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