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 (...) of artificial systems and reviews their desiderata. We provide a model that explicitly spells out the main concepts and relations necessary to consider and investigate when evaluating, adjusting, choosing, and developing explainability approaches that aim to satisfy stakeholders' desiderata. This model can serve researchers from the variety of different disciplines involved in XAI as a common ground. It emphasizes where there is interdisciplinary potential in the evaluation and the development of explainability approaches. (shrink)

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.

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.

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 (...) implicit biases provide epistemic. The first problem will be overcome by introducing presentational attitudes that are not in need of justification as basic ways of possessing epistemic reasons. The latter two problems will be solved by introducing epistemic rational capacities of two different kinds, which are exercised in mental states that are ways of possessing epistemic reasons, and by distinguishing these from mental states that are not exercises of epistemic rational capacities. (shrink)

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 (...) picture can make sense of the clash. (shrink)

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 (...) used to gener- ate sufficiently accurate as well as graspable rationalizing explanations for CI behavior. (shrink)