Bas van Fraassen has argued that explanatory reasoning does not provide confirmation for explanatory hypotheses because explanatory reasoning increases information and increasing information does not provide confirmation. We compare this argument with a skeptical argument that one should never add any beliefs because adding beliefs increases information and increasing information does not provide confirmation. We discuss the similarities between these two arguments and identify several problems with van Fraassen’s argument.

In the recent literature on the nature of knowledge, a rivalry has emerged between modalism and explanationism. According to modalism, knowledge requires that our beliefs track the truth across some appropriate set of possible worlds. Modalists tend to focus on two modal conditions: sensitivity and safety. According to explanationism, knowledge requires only that beliefs bear the right sort of explanatory relation to the truth. In slogan form: knowledge is believing something because it's true. In this paper, we aim to vindicate explanationism from some recent objections offered by Gualtiero Piccinini, Dario Mortini, and Kenneth Boyce and Andrew Moon. Together, these authors present five purported counterexamples to the sufficiency of the explanationist analysis for knowledge. In addition, Mortini devises a clever argument that explanationism entails the violation of a plausible closure principle on knowledge. We will argue that explanationism is innocent of all these charges against it, and we hope that the strength of the defense we offer of explanationism is evidence in its favor, and a reason to investigate explanationism further as the long-elusive truth about the nature of knowledge.

In response to widespread use of automated decision-making technology, some have considered a right to explanation. In this article, I draw on insights from philosophical work on explanation to present a series of challenges to this idea, showing that the normative motivations for access to such explanations ask for something difficult, if not impossible, to extract from automated systems. I consider an alternative, outcomes-focused approach to the normative evaluation of automated decision making and recommend it as a way to pursue the goods originally associated with explainability.

People often overestimate their understanding of how things work. For instance, people believe that they can explain even ordinary phenomena such as the operation of zippers and speedometers in greater depth than they really can. This is called the illusion of explanatory depth. Fortunately, a person can expose the illusion by attempting to generate a causal explanation for how the phenomenon operates (e.g., how a zipper works). This might be because explanation makes salient the gaps in a person’s knowledge of that phenomenon. However, recent evidence suggests that people might be able to expose the illusion by instead explaining a different phenomenon. Across three preregistered experiments, we tested whether the process of explaining one phenomenon (e.g., how a zipper works) would lead someone to report knowing less about a completely different phenomenon (e.g., how snow forms). In each experiment, we found that attempting to explain one phenomenon led participants to report knowing less about various phenomena. For example, participants reported knowing less about how snow forms after attempting to explain how a zipper works. We discuss alternative accounts of the illusion of explanatory depth that might better fit our results. We also consider the utility of explanation as an indirect, non-confrontational debiasing method in which a person generalizes a feeling of ignorance about one phenomenon to their knowledge base more generally.

Although the inferring of explanations plays an important role in both our everyday lives and in the workings of science, I argue that inference to the best explanation as it is commonly conceived is often not the best way to capture this sort of reasoning. I suggest that a different form of reasoning – so-called immediate explanatory inference – is instead often much better suited to this task. This is a form of inference in which we are justified in believing explanations for the evidence before us purely in virtue of this evidence, and not in virtue of the evidence plus some general principle or rule of non-deductive reasoning. I defend the idea of such a notion of inference, and argue that it plays a central role in both ordinary life and science.

Knowing what event precipitated a client's abnormal behaviors makes the client appear more normal than if the event is not known (Meehl, 1973). Does such knowledge also influence judgments of the need for psychological treatment, and if so, does it matter whether the precipitating event was inside or outside the client's control? We presented undergraduates with cases of hypothetical clients exhibiting abnormal behaviors and manipulated whether they were also told of a precipitating event explaining those behaviors. Knowing the precipitant significantly reduced perceptions of clients’ need for treatment, but only when the precipitating event was outside the client's control. These findings call into question the notion that it need always be beneficial for an outside reasoner to uncover the root cause of a client's psychological problems, particularly when the root cause is still unknown to the client. The rationality of the effect and additional implications for decision-making are discussed.

Previous work showed that people find explanations more satisfying when they contain irrelevant neuroscience information. The current studies investigate why this effect happens. In Study 1 ( N=322), subjects judged psychology explanations that did or did not contain irrelevant neuroscience information. Longer explanations were judged more satisfying, as were explanations containing neuroscience information, but these two factors made independent contributions. In Study 2 ( N=255), subjects directly compared good and bad explanations. Subjects were generally successful at selecting the good explanation except when the bad explanation contained neuroscience and the good one did not. Study 3 ( N=159) tested whether neuroscience jargon was necessary for the effect, or whether it would obtain with any reference to the brain. Responses to these two conditions did not differ. These results confirm that neuroscience information exerts a seductive effect on people’s judgments, which may explain the appeal of neuroscience information within the public sphere.

This paper reports two experiments comparing variants of multiple explanation applied in the early stages of a judgment task (a case involving employee theft) where participants are not given a menu of response options. Because prior research has focused on situations where response options are provided to judges, we identify relevant dependent variables that an intervention might affect when such options are not given. We use these variables to build a causal model of intervention that illustrates both the intended effects of multiple explanation and some potentially competing processes that it may trigger. Although multiple explanation clearly conveys some benefits (e.g., willingness to delay action to engage in information search, increased detail, quality and confidence in alternative explanations) in the present experiments, we also found evidence that it may initiate or enhance processes that attenuate its advantages (e.g., feelings that one does not need more data if one has multiple good explanations).

Transparency is widely acknowledged as a core value in the governance of artificial intelligence (AI) technologies. However, scholarship on AI technologies and their regulation often casts this need for transparency in terms of requirements for the explanation of algorithmic outputs and/or decisions produced with the involvement of opaque black-box AI systems. Our article argues that this discourse has re-interpreted and reshaped transparency in fundamental ways away from its original meaning. The target of transparency – in most cases, the provider of AI software – determines and shapes what is made visible to the outside world, and there is no external check on the validity and accuracy of such mediated accounts and explanations, opening transparency up for manipulation. Through a theoretically informed and critical analysis of the transparency provisions in the European Union’s AI Act proposal, the article shows that the substitution of transparency with mediated explanations faces important technical constraints, creates opportunities and incentives for both providers and public-sector users of AI systems to adopt opaque practices, and reinforces secrecy requirements that gag accountability in practice. An approach to transparency as disclosure thus becomes necessary, even if not sufficient in and of itself, to ensure the accountable development and use of AI technologies in the European Union. Transparency needs to be reclaimed as a core concept, accountability tailored and reinforced and the necessity for secrecy re-examined and cordoned off.