. Computational chemistry grew in a new era of “desktop modeling,” which coincided with a growing demand for modeling software, especially from the pharmaceutical industry. Parameterization of models in computational chemistry is an arduous enterprise, and we argue that this activity leads, in this specific context, to tensions among scientists regarding the epistemic opacity transparency of parameterized methods and the software implementing them. We relate one flame war from the Computational Chemistry mailing List in order to assess in (...) detail the relationships between modeling methods, parameterization, software and the various forms of their enclosure or disclosure. Our claim is that parameterization issues are an important and often neglected source of epistemic opacity and that this opacity is entangled in methods and software alike. Models and software must be addressed together to understand the epistemological tensions at stake. (shrink)

This paper looks at the debates between internalism and externalism in mind and epistemology. In each realm, internalists face what we call 'The Connection Problem', while externalists face what we call 'The Problem of Opacity'. We offer an integrated account of thought content and epistemic warrant that overcomes the problems. We then apply the framework to debates between internalists and externalists in metaethics.

Some how-possibly explanations have epistemic value because they are epistemically possible; we cannot rule out their truth. One paradoxical implication of that proposal is that epistemic value may be obtained from mere ignorance. For the less we know, then the more is epistemically possible. This chapter examines a particular class of problematic epistemically possible how-possibly explanations, viz. *epistemically opaque* how-possibly explanations. Those are how-possibly explanations justified by an epistemically opaque process. How could epistemically opaque how-possibly explanations have (...) class='Hi'>epistemic value if they result from a process about which we lack knowledge or understanding? This chapter proposes three different strategies to salvage epistemic value from epistemic opacity, namely salvaging value from 1) functional transparency, 2) modal operator interpretation, and 3) pursuitworthiness. It illustrates using cases from deep neural network modeling. (shrink)

Many jurisdictions prohibit or severely restrict the use of evidence about a defendant’s character to prove legal culpability. Situationists, who argue that conduct is largely determined by situational features rather than by character, can easily defend this prohibition. According to situationism, character evidence is misleading or paltry. -/- Proscriptions on character evidence seem harder to justify, however, on virtue ethical accounts. It appears that excluding character evidence either denies the centrality of character for explaining conduct—the situationist position—or omits probative evidence. (...) Situationism is, after all, presented as antithetical to virtue ethics. -/- This essay provides a virtue ethical defense of character evidence exclusion rules. We show that existing virtue ethical rebuttals to situationism themselves support prohibitions on character evidence; even if behavior arises from stable character traits, character evidence should be prohibited. In building our case, we provide a taxonomy of kinds of character judgment and reconcile the ubiquity and reasonableness of character judgments in ordinary life with the epistemic legitimacy of character evidence prohibitions in law. (shrink)

In this chapter what I call the “backside” of habit is explored. I am interested in the philosophical implications of the physical and physiological processes that mediate, and which allow for what comes to appear as almost magic; namely the various sensorimotor associations and integrations that allows us to replay our past experiences, and to in a certain sense perceive potential futures, and to act and bring about anticipated outcomes – without quite knowing how. Thus, the term “backside” is meant (...) to refer both the actual mediation and the epistemic opacity of these backstage intermediaries that allow for the front stage magic. The question is if the epistemic complexities around sensorimotor mediation gives us valuable insights into the nature of human agency and further how it might begin to show us new ways to think of the mind as truly embodied yet not reducible to any finite body-as-object. (shrink)

We motivate the idea that presupposition is a transparent attitude. We then explain why epistemic opacity is not a serious problem for Robert Stalnaker's theory of content and conversation. We conclude with critical remarks about John Hawthorne and Ofra Magidor's alternative theory.

Concerns over epistemic opacity abound in contemporary debates on Artificial Intelligence (AI). However, it is not always clear to what extent these concerns refer to the same set of problems. We can observe, first, that the terms 'transparency' and 'opacity' are used either in reference to the computational elements of an AI model or to the models to which they pertain. Second, opacity and transparency might either be understood to refer to the properties of AI systems (...) or to the epistemic situation of human agents with respect to these systems. While these diagnoses are independently discussed in the literature, juxtaposing them and exploring possible interrelations will help to get a view of the relevant distinctions between conceptions of opacity and their empirical bearing. In pursuit of this aim, two pertinent conditions affecting computer models in general and contemporary AI in particular are outlined and discussed: opacity as a problem of computational tractability and opacity as a problem of the universality of the computational method. (shrink)

One of the main worries with machine learning model opacity is that we cannot know enough about how the model works to fully understand the decisions they make. But how much is model opacity really a problem? This chapter argues that the problem of machine learning model opacity is entangled with non-epistemic values. The chapter considers three different stages of the machine learning modeling process that corresponds to understanding phenomena: (i) model acceptance and linking the model (...) to the phenomenon, (ii) explanation, and (iii) attributions of understanding. At each of these stages, non-epistemic values can, in part, determine how much machine learning model opacity poses a problem. (shrink)

Google Search is arguably one of the primary epistemic tools in use today, with the lion’s share of the search-engine market globally. Scholarship on countering the current scourge of misinformation often recommends “digital lit- eracy” where internet users, especially those who get their information from so- cial media, are encouraged to fact-check such information using reputable sources. Given our current internet-based epistemic landscape, and Google’s dominance of the internet, it is very likely that such acts of epistemic (...) hygiene will take place via Google Search. The question arises whether Google Search is fit for purpose, given the apparent misalignment the general epistemic goal of promoting true beliefs and the greater online commercial ecosystem in which it is embedded. I argue that Google Search is epistemically problematic as it stands, mainly due to the opacity related to the parameters it uses for personalising search results. I further argue that in as far as an ordinary internet user is legitimately ignorant of Google’s workings, uses it in an “ordinary manner”, and is generally unable to avoid using it in the current information environment, they are not ep- istemically blameworthy for any false beliefs that they acquire via it. I conclude that too much emphasis is currently placed on individual epistemic practices and not enough on our information environment and epistemic tools when it comes to countering misinformation. (shrink)

Self-knowledge presents a challenge for naturalistic theories of mind. Peter Carruthers’s (2011) approach to this challenge is Rylean: He argues that we know our own propositional attitudes because we (unconsciously) interpret ourselves, just as we have to interpret others in order to know theirs’. An alternative approach, opposed by Carruthers, is to argue that we do have a special access to our own beliefs, but that this is a natural consequence of our reasoning capacity. This is the approach of transparency (...) theories of self-knowledge, neatly encapsulated in Byrne’s epistemic rule (BEL): If p, believe that you believe that p (Byrne 2005). In this paper, I examine an objection to Carruthers’s theory in order to see whether it opens up space for a transparency theory of self-knowledge: Is it not the case that in order to interpret someone I have to have some direct access to what I believe (cf. Friedman and Petrashek 2009)? (shrink)

Computational systems biologists create and manipulate computational models of biological systems, but they do not always have straightforward epistemic access to the content and behavioural profile of such models because of their length, coding idiosyncrasies, and formal complexity. This creates difficulties both for modellers in their research groups and for their bioscience collaborators who rely on these models. In this paper we introduce a new kind of visualization that was developed to address just this sort of epistemic (...) class='Hi'>opacity. The visualization is unusual in that it depicts the dynamics and structure of a computer model instead of that model’s target system, and because it is generated algorithmically. Using considerations from epistemology and aesthetics, we explore how this new kind of visualization increases scientific understanding of the content and function of computer models in systems biology to reduce epistemic opacity. (shrink)

Simple idealized models seem to provide more understanding than opaque, complex, and hyper-realistic models. However, an increasing number of scientists are going in the opposite direction by utilizing opaque machine learning models to make predictions and draw inferences, suggesting that scientists are opting for models that have less potential for understanding. Are scientists trading understanding for some other epistemic or pragmatic good when they choose a machine learning model? Or are the assumptions behind why minimal models provide understanding misguided? (...) In this paper, using the case of deep neural networks, I argue that it is not the complexity or black box nature of a model that limits how much understanding the model provides. Instead, it is a lack of scientific and empirical evidence supporting the link that connects a model to the target phenomenon that primarily prohibits understanding. (shrink)

Computer simulations of the formation and evolution of large-scale structure in the universe are integral to the enterprise of modern cosmology. Establishing the reliability of these simulations has been extremely challenging, primarily because of epistemic opacity. In this setting, robustness analysis defined by requiring converging outputs from a diverse ensemble of simulations is insufficient to determine simulation validity. We propose an alternative path of structured code validation that applies eliminative reasoning to isolate and reduce possible sources of error, (...) a potential path that is already being explored by some cosmologists. (shrink)

It is generally assumed that if it is possible to believe that p without believing that q, then there is some difference between the object of the thought that p and the object of the thought that q. This assumption is challenged in the present paper, opening the way to an account of epistemic opacity that improves on existing accounts, not least because it casts doubt on various arguments that attempt to derive startling ontological conclusions from seemingly innocent (...) epistemic premises. (shrink)

The increasing demand for transparency in AI has recently come under scrutiny. The question is often posted in terms of “epistemic double standards”, and whether the standards for transparency in AI ought to be higher than, or equivalent to, our standards for ordinary human reasoners. I agree that the push for increased transparency in AI deserves closer examination, and that comparing these standards to our standards of transparency for other opaque systems is an appropriate starting point. I suggest that (...) a more fruitful exploration of this question will involve a different comparison class. We routinely treat judgments made by highly trained experts in specialized fields as fair or well-grounded even though—by the nature of expert/layperson division of epistemic labor—an expert will not be able to provide an explanation of the reasoning behind these judgments that makes sense to most other people. Regardless, laypeople are thought to be acting reasonably—and ethically—in deferring to the judgments of experts that concern their areas of specialization. I suggest that we reframe our question regarding the appropriate standards of transparency in AI as one that asks when, why, and to what degree it would be ethical to accept opacity in AI. I argue that our epistemic relation to certain opaque AI technology may be relevantly similar to the layperson’s epistemic relation to the expert in certain respects, such that the successful expert/layperson division of epistemic labor can serve as a blueprint for the ethical use of opaque AI. (shrink)

Psychedelic ingestion and meditative practice are both ancient methods for altering consciousness that became widely known in Western society in the second half of the 20th century. Do the similarities begin and end there, or do these methods – as many have claimed over the years – share some deeper common elements? In this chapter I take a neurophilosophical approach to this question and argue that there are, indeed, deeper commonalities. Recent empirical studies show that psychedelics and meditation modulate overlapping (...) brain networks involved in the sense of self, salience, and attention; moreover, psychedelics can occasion lasting increases in “mindfulness-related capacities” for taking a non-reactive stance on one’s inner experience (e.g. Sampedro et al. 2017). The self-binding theory of psychedelic ego dissolution (Letheby and Gerrans 2017) offers a plausible explanation of these findings: by disrupting self-related beliefs implemented in high-level cortical networks, both psychedelics and meditation can “unbind” mental contents from one’s self-model, moving these contents along the continuum from phenomenal transparency to opacity (cf. Metzinger 2003). In other words, both psychedelics and meditation can expose and weaken our foundational beliefs about our own identity, allowing us to disidentify with these beliefs and see them as “just thoughts”. There are connections between these ideas and recent arguments suggesting that psychedelic use may have epistemic benefits consistent with philosophical naturalism (Letheby 2015, 2016, 2019). I conclude with a proposal: these connections may help in thinking about the putative epistemic benefits of meditation practice from a naturalistic perspective. (shrink)

The relevance of any logical analysis lies in its ability to solve paradoxes and trace conceptual troubles back; with this respect, the task of epistemic logic is to handle paradoxes in connection with the concept of knowledge. Epistemic logic is currently introduced as the logical analysis of crucial concepts within epistemology, namely: knowledge, belief, truth, and justification. An alternative approach will be advanced here in order to enlighten such a discourse, as centred upon the word assertion and displayed (...) in terms of utterance. Insofar as epistemic modalities express some attitudes, the intentionality of discourse will be emphasized within an illocutionary modal logic. Two large views will range over the whole study: declarative and epistemic sentences have one and the same logic (assertion logic); the plurality of languages games doesn’t entail any logical pluralism. -/- La valeur d'une analyse logique réside dans sa capacité à résoudre des paradoxes et à comprendre l'origine de nos problèmes conceptuels ; à ce titre, le rôle d'une logique épistémique est de traiter des paradoxes liés au concept de connaissance. On présente généralement la logique épistémique comme une analyse logique de concepts centraux en épistémologie : connaissance, croyance, vérité, justification. Une autre approche sera proposée ici en vue de clarifier ce genre de discours, centrée sur la notion d'assertion et décrite en termes d'énonciation. Parce que les modalités épistémiques expriment des attitudes, c'est l'intentionnalité du discours qui sera mise en valeur dans le cadre d'une logique modale illocutoire. Deux thèses transversales parcourront l'ensemble du travail : les énoncés déclaratifs et épistémiques partagent la même logique (logique assertorique) ; la pluralité des jeux de langages n'implique pas un pluralisme logique. (shrink)

The opacity of some recent Machine Learning (ML) techniques have raised fundamental questions on their explainability, and created a whole domain dedicated to Explainable Artificial Intelligence (XAI). However, most of the literature has been dedicated to explainability as a scientific problem dealt with typical methods of computer science, from statistics to UX. In this paper, we focus on explainability as a pedagogical problem emerging from the interaction between lay users and complex technological systems. We defend an empirical methodology based (...) on field work, which should go beyond the in-vitro analysis of UX to examine in-vivo problems emerging in the field. Our methodology is also comparative, as it chooses to steer away from the almost exclusive focus on ML to compare its challenges with those faced by more vintage algorithms. Finally, it is also philosophical, as we defend the relevance of the philosophical literature to define the epistemic desiderata of a good explanation. This study was conducted in collaboration with Etalab, a Task Force of the French Prime Minister in charge of Open Data & Open Government Policies, dealing in particular with the enforcement of the right to an explanation. In order to illustrate and refine our methodology before going up to scale, we conduct a preliminary work of case studies on the main different types of algorithms used by the French administration: computation, matching algorithms and ML. We study the merits and drawbacks of a recent approach to explanation, which we baptize input-output black box reasoning or BBR for short. We begin by presenting a conceptual framework including the distinctions necessary to a study of pedagogical explainability. We proceed to algorithmic case studies, and draw model-specific and model-agnostic lessons and conjectures. (shrink)

This chapter focuses on the connection between moral disagreement and moral relativism. Moral relativists, generally speaking, think both (i) that there is no unique objectively correct moral standard and (ii) that the rightness and wrongness of an action depends in some way on a moral standard accepted by some group or an individual. This chapter will first consider the metaphysical and epistemic arguments for moral relativism that begin from the premise that there is considerable amount of moral disagreement both (...) within individual societies and between them. The second half of the chapter, by contrast, focuses on the objection that moral relativism threatens to make us unable to have moral disagreements because it seems to make us speak past one another. This part of the chapter also evaluates relativist responses to this disagreement problem that rely on semantic opacity, disagreement in attitude, metalinguistic negotiations, and truth relativism. The chapter finally concludes by considering future directions of research in this area. (shrink)

Definition of the problem The umbrella term “explicability” refers to the reduction of opacity of artificial intelligence (AI) systems. These efforts are challenging for medical AI applications because higher accuracy often comes at the cost of increased opacity. This entails ethical tensions because physicians and patients desire to trace how results are produced without compromising the performance of AI systems. The centrality of explicability within the informed consent process for medical AI systems compels an ethical reflection on the (...) trade-offs. Which levels of explicability are needed to obtain informed consent when utilizing medical AI? Arguments We proceed in five steps: First, we map the terms commonly associated with explicability as described in the ethics and computer science literature, i.e., disclosure, intelligibility, interpretability, and explainability. Second, we conduct a conceptual analysis of the ethical requirements for explicability when it comes to informed consent. Third, we distinguish hurdles for explicability in terms of epistemic and explanatory opacity. Fourth, this then allows to conclude the level of explicability physicians must reach and what patients can expect. In a final step, we show how the identified levels of explicability can technically be met from the perspective of computer science. Throughout our work, we take diagnostic AI systems in radiology as an example. Conclusion We determined four levels of explicability that need to be distinguished for ethically defensible informed consent processes and showed how developers of medical AI can technically meet these requirements. (shrink)

It is widely held that counterfactuals, unlike attitude ascriptions, preserve the referential transparency of their constituents, i.e., that counterfactuals validate the substitution of identicals when their constituents do. The only putative counterexamples in the literature come from counterpossibles, i.e., counterfactuals with impossible antecedents. Advocates of counterpossibilism, i.e., the view that counterpossibles are not all vacuous, argue that counterpossibles can generate referential opacity. But in order to explain why most substitution inferences into counterfactuals seem valid, counterpossibilists also often maintain that (...) counterfactuals with possible antecedents are transparency‐preserving. I argue that if counterpossibles can generate opacity, then so can ordinary counterfactuals with possible antecedents. Utilizing an analogy between counterfactuals and attitude ascriptions, I provide a counterpossibilist‐friendly explanation for the apparent validity of substitution inferences into counterfactuals. I conclude by suggesting that the debate over counterpossibles is closely tied to questions concerning the extent to which counterfactuals are more like attitude ascriptions and epistemic operators than previously recognized. (shrink)

This paper analyses the phenomenology and epistemology of chatbots such as ChatGPT and Bard. The computational architecture underpinning these chatbots are large language models (LLMs), which are generative artificial intelligence (AI) systems trained on a massive dataset of text extracted from the Web. We conceptualise these LLMs as multifunctional computational cognitive artifacts, used for various cognitive tasks such as translating, summarizing, answering questions, information-seeking, and much more. Phenomenologically, LLMs can be experienced as a “quasi-other”; when that happens, users anthropomorphise them. (...) For most users, current LLMs are black boxes, i.e., for the most part, they lack data transparency and algorithmic transparency. They can, however, be phenomenologically and informationally transparent, in which case there is an interactional flow. Anthropomorphising and interactional flow can, in some users, create an attitude of (unwarranted) trust towards the output LLMs generate. We conclude this paper by drawing on the epistemology of trust and testimony to examine the epistemic implications of these dimensions. Whilst LLMs generally generate accurate responses, we observe two epistemic pitfalls. Ideally, users should be able to match the level of trust that they place in LLMs to the degree that LLMs are trustworthy. However, both their data and algorithmic opacity and their phenomenological and informational transparency can make it difficult for users to calibrate their trust correctly. The effects of these limitations are twofold: users may adopt unwarranted attitudes of trust towards the outputs of LLMs (which is particularly problematic when LLMs hallucinate), and the trustworthiness of LLMs may be undermined. (shrink)

In the present paper I defend an interpretation of the Cartesian notion of material falsity that it would be adequate to describe as ‘epistemic’, as opposed to most other views in the literature, which could be described as ‘metaphysical’. Whereas metaphysical conceptions of material falsity consider an idea to be such because of some kind of failure in their representative properties, that is, in the relation between what they exhibit and their objects, an epistemic view considers that what (...) makes an idea materially false is some kind of opacity that precludes their representational properties to be known in the first place. In the paper the evidence for these two kinds of reading is assessed, and it is shown that epistemic views are clearly more adequate. (shrink)

I present an approach to our conceiving absolute impossibilities—things which obtain at no possible world—in terms of ceteris paribus intentional operators: variably restricted quantifiers on possible and impossible worlds based on world similarity. The explicit content of a representation plays a role similar in some respects to the one of a ceteris paribus conditional antecedent. I discuss how such operators invalidate logical closure for conceivability, and how similarity works when impossible worlds are around. Unlike what happens with ceteris paribus counterfactual (...) conditionals, the closest worlds are relevantly closest belief-worlds: closest to how things are believed to be, rather than to how they are. Also, closeness takes into account apriority and the opacity of intentional contexts. (shrink)

The ongoing explosion of interest in artificial intelligence is fueled in part by recently developed techniques in machine learning. Those techniques allow automated systems to process huge amounts of data, utilizing mathematical methods that depart from traditional statistical approaches, and resulting in impressive advancements in our ability to make predictions and uncover correlations across a host of interesting domains. But as is now widely discussed, the way that those systems arrive at their outputs is often opaque, even to the experts (...) who design and deploy them. Is it morally problematic to make use of opaque automated methods when making high-stakes decisions, like whether to issue a loan to an applicant, or whether to approve a parole request? Many scholars answer in the affirmative. However, there is no widely accepted explanation for why transparent systems are morally preferable to opaque systems. We argue that the use of automated decision-making systems sometimes violates duties of consideration that are owed by decision-makers to decision-subjects, duties that are both epistemic and practical in character. Violations of that kind generate a weighty consideration against the use of opaque decision systems. In the course of defending our approach, we show that it is able to address three major challenges sometimes leveled against attempts to defend the moral import of transparency in automated decision-making. (shrink)

Artificial intelligence (AI) and machine learning (ML) models have undoubtedly become useful tools in science. In general, scientists and ML developers are optimistic – perhaps rightfully so – about the potential that these models have in facilitating scientific progress. The philosophy of AI literature carries a different mood. The attention of philosophers remains on potential epistemological issues that stem from the so-called “black box” features of ML models. For instance, Eamon Duede (2023) argues that opacity in deep learning models (...) (DLMs) is epistemically problematic in the context of justification, though not in the context of discovery. -/- In this paper, I aim to show that a similar epistemological concern is echoed in the epistemology of imagination literature. It is traditionally held that, given its black box features, reliance on the imagination is epistemically problematic in the context of justification, though not in the context of discovery. The constraints-based approach to the imagination answers the epistemological concern by providing an account of how we can rely on the imagination in the context of justification by way of constraints. I argue by analogy that a similar approach can be applied to the opaque DLM case. Ultimately, my goal is to explore just how far this analogy extends, and whether a constraints-based approach to opaque DLMs can answer the epistemological concern surrounding their black box features in the context of justification. -/- (Note that this paper is IN PROGRESS and UNPUBLISHED). (shrink)

One challenge in developing an account of the nature of epistemic blame is to explain what differentiates epistemic blame from mere negative epistemic evaluation. The challenge is to explain the difference, without invoking practices or behaviors that seem out of place in the epistemic domain. In this paper, I examine whether the most sophisticated recent account of the nature of epistemic blame—due to Jessica Brown—is up for the challenge. I argue that the account ultimately falls (...) short, but does so in an instructive way. Drawing on the lessons learned, I put forward an alternative approach to the nature of epistemic blame. My account understands epistemic blame in terms of modifications to the intentions and expectations that comprise our “epistemic relationships” with one another. This approach has a number of attractions shared by Brown’s account, but it can also explain the significance of epistemic blame. (shrink)

Imagine a character, Mary Analogue, who has a complete theoretical knowledge of her subject matter: the illusory nature of self. Suppose that when presenting her paper on no self at a conference she suffers stage-fright – a reaction that implies she is under an illusion of the very self whose existence she denies. Might there be something defective about her knowledge of no self? The Buddhist tradition would claim that Mary Analogue, despite her theoretical omniscience, lacks deep ‘insight knowledge’ into (...) the reality of no self. The only way for her to gain insight, and thereby improve her epistemic status, would be to divest her mind of the self-illusion. In this paper, I offer an analysis of what could be epistemically involved in the process of acquiring such insight knowledge whereby one becomes, in Buddhist parlance, ‘awakened’. (shrink)

Jennifer Nagel (2010) has recently proposed a fascinating account of the decreased tendency to attribute knowledge in conversational contexts in which unrealized possibilities of error have been mentioned. Her account appeals to epistemic egocentrism, or what is sometimes called the curse of knowledge, an egocentric bias to attribute our own mental states to other people (and sometimes our own future and past selves). Our aim in this paper is to investigate the empirical merits of Nagel’s hypothesis about the psychology (...) involved in knowledge attribution. (shrink)

ABSTRACT The testimonies of celebrities affect the lives of their many followers who pay attention to what they say. This gives celebrities a high degree of epistemic power, which has come under scrutiny during the COVID-19 pandemic. This paper investigates the duties that arise from this power. We argue that celebrities have a negative duty of testimonial justice not to undermine trust in authoritative sources by spreading misinformation or directing attention to untrustworthy sources. Moreover, celebrities have a general imperfect (...) duty to try to correct for an unjust distribution of attention by redirecting it to those who deserve it. During a pandemic this may become a perfect one, due to the harm that could be prevented if people follow the advice of experts. Relatedly, we argue that celebrities have an imperfect duty to promote behavior that will reduce the spread of a pandemic. We outline three ways they might do so: they might take on the position of a role model, they may act as a salience magnet or they can direct people’s attention towards others who have taken on these roles. (shrink)

It is clear that lack of awareness of the consequences of an action can undermine moral responsibility and blame for these consequences. But when and how it does so is controversial. Sometimes an agent believing that the outcome might occur is excused because it seemed unlikely to her, and sometimes an agent having no idea that it would occur is nevertheless to blame. A low or zero degree of belief might seem to excuse unless the agent “should have known better”, (...) but it is unclear how to spell out this normative condition. -/- This chapter combines (a) an independently motivated account of responsibility, blame, and credit as grounded in a normal explanatory relation between agential qualities and objects of responsibility with (b) the familiar Strawsonian idea that moral blame and credit depend on the agent’s quality of will. The resulting explanatory quality of will condition on moral responsibility is then further motivated by being shown to account for the effects on moral blame and credit of justifications, as well as of excuses and undermined control in cases not involving ignorance. -/- The explanatory quality of will condition is finally applied to cases involving various degrees of lack of awareness. Though this condition itself involves no awareness requirement, it is shown how it accounts for the degrees to which lack of awareness can excuse. It is also explained how lack of awareness fails as an excuse exactly when the agent should have known better and can be blamed for not doing so. (shrink)

Responding rationally to the information that others disagree with one’s beliefs requires assessing the epistemic credentials of the opposing beliefs. Conciliatory accounts of disagreement flow in part from holding that these assessments must be independent from one’s own initial reasoning on the disputed matter. I argue that this claim, properly understood, does not have the untoward consequences some have worried about. Moreover, some of the difficulties it does engender must be faced by many less conciliatory accounts of disagreement.

We argue that the interaction of biased media coverage and widespread employment of the recognition heuristic can produce epistemic injustices. First, we explain the recognition heuristic as studied by Gerd Gigerenzer and colleagues, highlighting how some of its components are largely external to, and outside the control of, the cognitive agent. We then connect the recognition heuristic with recent work on the hypotheses of embedded, extended, and scaffolded cognition, arguing that the recognition heuristic is best understood as an instance (...) of scaffolded cognition. In section three, we consider the double-edged sword of cognitive scaffolding. On the one hand, scaffolds can reduce the internal processing demands on cognitive agents while increasing their access to information. On the other hand, the use of scaffolding leaves cognitive agents increasingly vulnerable to forming false beliefs or failing to form beliefs at all about particular topics. With respect to the recognition heuristic, agents rely on third parties (such as the media) to report not just what’s true but also what’s important or valuable. This makes cognitive agents relying on these third parties vulnerable to two erroneous influences: 1) because they don’t recognize something, it isn’t important or valuable, and 2) because they do recognize something, it is important or valuable. Call the latter the Kardashian Inference and the former the Darfur Inference. In section four, we use Fricker’s (2007) concept of epistemic injustice to characterize the nature and harm of these false inferences, with special emphasis on the Darfur Inference. In section five, we use data-mining and an empirical study to show how Gigerenzer’s population estimation task is liable to produce Darfur Inferences. We conclude with some speculative remarks on more important Darfur Inferences, and how to avoid them by scaffolding better. One primary way to accomplish this it to shift the burden of embodying the virtue of epistemic justice from the hearer or consumer of media to the media themselves. (shrink)

Classrooms are unlevel knowing fields, contested terrains where knowledge and ignorance are produced and circulate with equal vigor, and where members of dominant groups are accustomed to having an epistemic home-terrain advantage. My project focuses on one form of resistance that regularly surfaces in discussions with social-justice content. Privilege-preserving epistemic pushback is a variety of willful ignorance that many members of dominant groups engage in when asked to consider both the lived and structural injustices that members of marginalized (...) groups experience daily. I argue that this dominant form of resistance is neither an expression of skepticism nor a critical-thinking practice. I suggest that standard philosophical engagements with these expressions of resistance are incapable of tracking the harms of privilege-preserving epistemic pushback. I recommend treating this pushback as a “shadow text,” that is, as a text that runs alongside the readings in ways that offer no epistemic friction. I offer this as one critical philosophical practice for making students mindful of the ways they contribute to the circulation of ignorance and epistemic violence during the course of their discussions. (shrink)

In one of the earlier influential papers in the field of experimental philosophy titled Normativity and Epistemic Intuitions published in 2001, Jonathan M. Weinberg, Shaun Nichols and Stephen Stich reported that respondents answered Gettier type questions differently depending on their ethnic background as well as socioeconomic status. There is currently a debate going on, on the significance of the results of Weinberg et al. (2001) and its implications for philosophical methodology in general and epistemology in specific. Despite the debates, (...) however, to our knowledge, there has not been a replication attempt of the experiments of the original paper. We collected data from four different sources (two on-line and two in-person) to replicate the experiments. Despite several different data sets and in various cases larger sample sizes and hence greater power to detect differences, we failed to detect significant differences between the above-mentioned ethnic and socioeconomic groups. Our results suggest that epistemic intuitions are more robust across ethnic and socioeconomic groups than Weinberg et al. (2001) indicates. Given our data, we believe that the notion of differences in epistemic intuitions among different ethnic and socioeconomic groups that follows from Weinberg et al. (2001) needs to be corrected. (shrink)

This paper examines how radical realism, a form of ideology critique grounded in epistemic rather than moral normativity, can illuminate the relationship between ideology and political power. The paper argues that radical realism can has both an evaluative and a diagnostic function. Drawing on reliabilist epistemology, the evaluative function shows how beliefs shaped by power differentials are often epistemically unwarranted, e.g. due to the influence of motivated reasoning and the suppression of critical scrutiny. The paper clarifies those mechanisms in (...) order to address some recent critiques of radical realism. The paper then builds on those clarifications to explore the how tracing the genealogy of legitimation stories can diagnose the distribution of power in society, even if ideology does not play a direct stabilising role. This diagnostic function creates a third position in the debate on ideology between culturalists and classical Marxists, and it can help reconciling aspects of structural and relational theories of power. (shrink)

In this paper, I characterize visual epistemic representations as concrete two- or three-dimensional tools for conveying information about aspects of their target systems or phenomena of interest. I outline two features of successful visual epistemic representation: that the vehicle of representation contain sufficiently accurate information about the phenomenon of interest for the user’s purpose, and that it convey this information to the user in a manner that makes it readily available to her. I argue that actual epistemic (...) representation may involve tradeoffs between these and is successful to the extent that they are present. (shrink)

One of metaepistemology’s most central debates revolves around the question of what the source of epistemic normativity is. Epistemic instrumentalism claims that epistemic normativity is a species of means-ends normativity. One of the most prominent objections against epistemic instrumentalism features cases of epistemic indifference: Cases where there’s evidence that p yet believing that p wouldn’t promote any of the agent’s aims, wants, or needs. Still, there’s an epistemic reason for the agent to believe that (...) p and thus epistemic instrumentalism is false. In response, instrumentalists have modified their views in various ways, with new contributions still forthcoming. Here, we investigate a neglected aspect of this debate: Laypeople’s judgments on cases of epistemic indifference. In two studies, we investigated whether laypeople agree with the verdict in cases of epistemic indifference as well as the key ideas behind the more recent instrumentalist replies. Our findings indicate that a significant amount of participants found it hard to “buy into” the cases of epistemic indifference as Kelly has constructed them. Participants did generally share Kelly’s judgment in cases of epistemic indifference. Lastly, some instrumentalist replies are well suited to explain participants’ judgments that agents ought to believe in cases of epistemic indifference. (shrink)

Typically, nudging is a technique for steering the choices of people without giving reasons or using enforcement. In benevolent cases, it is used when people are insufficiently responsive to reason. The nudger triggers automatic cognitive mechanisms – sometimes even biases – in smart ways in order to push irrational people in the right direction. Interestingly, this technique can also be applied to doxastic attitudes. Someone who is doxastically unresponsive to evidence can be nudged into forming true beliefs or doxastic attitudes (...) that are propositionally justified. When doxastic nudging uses non-rational mechanisms, the worry is that nudging cannot result in justified beliefs or knowledge, as the resulting doxastic attitudes lack the right kind of basis. In this paper, I will argue that given the right background views about knowledge, justified beliefs and the relevant processes, epistemic nudging is possible even in these cases. That is, all kinds of nudging can – in appropriate circumstances – produce justified beliefs or knowledge in the nudgee. (shrink)

This essay outlines an empirically grounded account of normative political legitimacy. The main idea is to give a normative edge to empirical measures of sociological legitimacy through a nonmoralized form of ideology critique. A power structure’s responsiveness to the values of those subjected to its authority can be measured empirically and may be explanatory or predictive insofar as it tracks belief in legitimacy, but by itself it lacks normative purchase. It merely describes a preference alignment, and so tells us nothing (...) about whether the ruled have reason to support the rulers. I argue that we can close this gap by filtering the preferences of the ruled through a form of nonmoralized epistemic ideology critique, itself grounded in an empirical account of how belief in legitimacy is formed. (shrink)

In this article, we argue that John Henry Newman was right to think that our passional nature can play a legitimate epistemic role. First, we unpack the standard objection to Newman’s understanding of the relationship between our passional nature and the evidential basis of faith. Second, we argue that the standard objection to Newman operates with a narrow definition of evidence. After challenging this notion, we then offer a broader and more humane understanding of evidence. Third, we survey recent (...) scholarship arguing that emotions, a key aspect of our passional nature, are cognitive. In this light, they plausibly have a proper epistemic role. Fourth, we defend Newman’s reliance on the passional nature in epistemic matters by showing how reasonable it is in light of this recent work on evidence and the nature of emotions. Newman’s insistence that the formation of a right state of heart and mind is crucial for epistemic success is far from untenable. (shrink)

We explore some possibilities for developing epistemic logic using truthmaker semantics. We identify three possible targets of analysis for the epistemic logician. We then list some candidate epistemic principles and review the arguments that render some controversial. We then present the classic Hintikkan approach to epistemic logic and note—as per the ‘problem of logical omniscience’—that it validates all of the aforementioned principles, controversial or otherwise. We then lay out a truthmaker framework in the style of Kit (...) Fine and present six different ways of extending this semantics with a conditional knowledge operator, drawing on notions of implication and content that are prominent in Fine’s work. We demonstrate that different logics are thereby generated, bearing on the aforementioned epistemic principles. Finally, we offer preliminary observations about the prospects for each logic. (shrink)

Within the United States, the most prominent justification for criminal punishment is retributivism. This retributivist justification for punishment maintains that punishment of a wrongdoer is justified for the reason that she deserves something bad to happen to her just because she has knowingly done wrong—this could include pain, deprivation, or death. For the retributivist, it is the basic desert attached to the criminal’s immoral action alone that provides the justification for punishment. This means that the retributivist position is not reducible (...) to consequentialist considerations nor in justifying punishment does it appeal to wider goods such as the safety of society or the moral improvement of those being punished. A number of sentencing guidelines in the U.S. have adopted desert as their distributive principle, and it is increasingly given deference in the “purposes” section of state criminal codes, where it can be the guiding principle in the interpretation and application of the code’s provisions. Indeed, the American Law Institute recently revised the Model Penal Code so as to set desert as the official dominate principle for sentencing. And courts have identified desert as the guiding principle in a variety of contexts, as with the Supreme Court’s enthroning retributivism as the “primary justification for the death penalty.” While retributivism provides one of the main sources of justification for punishment within the criminal justice system, there are good philosophical and practical reasons for rejecting it. One such reason is that it is unclear that agents truly deserve to suffer for the wrongs they have done in the sense required by retributivism. In the first section, I explore the retributivist justification of punishment and explain why it is inconsistent with free will skepticism. In the second section, I then argue that even if one is not convinced by the arguments for free will skepticism, there remains a strong epistemic argument against causing harm on retributivist grounds that undermines both libertarian and compatibilist attempts to justify it. I maintain that this argument provides sufficient reason for rejecting the retributive justification of criminal punishment. I conclude in the third section by briefly sketching my public health-quarantine model, a non-retributive alternative for addressing criminal behavior that draws on the public health framework and prioritizes prevention and social justice. I argue that the model is not only consistent with free will skepticism and the epistemic argument against retributivism, it also provides the most justified, humane, and effective way of dealing with criminal behavior. (shrink)

What if your peers tell you that you should disregard your perceptions? Worse, what if your peers tell you to disregard the testimony of your peers? How should we respond if we get evidence that seems to undermine our epistemic rules? Several philosophers have argued that some epistemic rules are indefeasible. I will argue that all epistemic rules are defeasible. The result is a kind of epistemic particularism, according to which there are no simple rules connecting (...) descriptive and normative facts. I will argue that this type of particularism is more plausible in epistemology than in ethics. The result is an unwieldy and possibly infinitely long epistemic rule — an Uber-rule. I will argue that the Uber-rule applies to all agents, but is still defeasible — one may get misleading evidence against it and rationally lower one’s credence in it. (shrink)

In a possible world framework, an agent can be said to know a proposition just in case the proposition is true at all worlds that are epistemically possible for the agent. Roughly, a world is epistemically possible for an agent just in case the world is not ruled out by anything the agent knows. If a proposition is true at some epistemically possible world for an agent, the proposition is epistemically possible for the agent. If a proposition is true at (...) all epistemically possible worlds for an agent, the proposition is epistemically necessary for the agent, and as such, the agent knows the proposition. -/- This framework presupposes an underlying space of worlds that we can call epistemic space. Traditionally, worlds in epistemic space are identified with possible worlds, where possible worlds are the kinds of entities that at least verify all logical truths. If so, given that epistemic space consists solely of possible worlds, it follows that any world that may remain epistemically possible for an agent verifies all logical truths. As a result, all logical truths are epistemically necessary for any agent, and the corresponding framework only allows us to model logically omniscient agents. This is a well-known consequence of the standard possible world framework, and it is generally taken to imply that the framework cannot be used to model non-ideal agents that fall short of logical omniscience. -/- A familiar attempt to model non-ideal agents within a broadly world involving framework centers around the use of impossible worlds where the truths of logic can be false. As we shall see, if we admit impossible worlds where “anything goes” in epistemic space, it is easy to avoid logical omniscience. If any logical falsehood is true at some impossible world, then any logical falsehood may remain epistemically possible for some agent. As a result, we can use an impossible world involving framework to model extremely non-ideal agents that do not know any logical truths. -/- A much harder, and considerably less investigated challenge is to ensure that the resulting epistemic space can also be used to model moderately ideal agents that are not logically omniscient but nevertheless logically competent. Intuitively, while such agents may fail to rule out impossible worlds that verify complex logical falsehoods, they are nevertheless able to rule out impossible worlds that verify obvious logical falsehoods. To model such agents, we need a construction of a non-trivial epistemic space that partly consists of impossible worlds where not "anything goes". This involves imposing substantive constraints on impossible worlds to eliminate from epistemic space, say, trivially impossible worlds that verify obvious logical falsehoods. -/- The central aim of this dissertation is to investigate the nature of such non-trivially impossible worlds and the corresponding epistemic spaces. To flag my conclusions, I argue that successful constructions of epistemic spaces that can safely navigate between the Charybdis of logical omniscience and the Scylla of of “anything goes” are hard, if not impossible to find. (shrink)

The paper discusses approaches to Epistemic Contextualism that model the satisfaction of the predicate ‘know’ in a given context C in terms of the notion of belief/fact-matching throughout a contextually specified similarity sphere of worlds that is centred on actuality. The paper offers three counterexamples to approaches of this type and argues that they lead to insurmountable difficulties. I conclude that what contextualists (and Subject-Sensitive Invariantists) have traditionally called the ‘epistemic standards’ of a given context C cannot be (...) explicated in terms of a contextually specified similarity sphere that is centred on actuality. The mentioned accounts of epistemic relevance and thus the corresponding accounts of the context-sensitivity (or subject-sensitivity) of ‘knows’ are to be rejected. (shrink)

The recent proliferation of deepfakes and other digitally produced deceptive representations has revived the debate on the epistemic robustness of photography and other mechanically produced images. Authors such as Rini (2020) and Fallis (2021) claim that the proliferation of deepfakes pose a serious threat to the reliability and the epistemic value of photographs and videos. In particular, Fallis adopts a Skyrmsian account of how signals carry information (Skyrms, 2010) to argue that the existence of deepfakes significantly reduces the (...) information that images carry about the world, which undermines their reliability as a source of evidence. In this paper, we focus on Fallis’ version of the challenge, but our results can be generalized to address similar pessimistic views such as Rini’s. More generally, we offer an account of the epistemic robustness of photography and videos that allows us to understand these systems of representation as continuous with other means of information transmission we find in nature. This account will then give us the necessary tools to put Fallis’ claims into perspective: using a richer approach to animal signaling based on the signaling model of communication (Maynard-Smith and Harper, 2003), we will claim that, while it might be true that deepfake technology increases the probability of obtaining false positives, the dimension of the epistemic threat involved might still be negligible. (shrink)

In this paper, we analyse how GPS-based navigation systems are transforming some of our intellectual virtues and then suggest two strategies to improve our practices regarding the use of such epistemic tools. We start by outlining the two main approaches in virtue epistemology, namely virtue reliabilism and virtue responsibilism. We then discuss how navigation systems can undermine five epistemic virtues, namely memory, perception, attention, intellectual autonomy, and intellectual carefulness. We end by considering two possible interlinked ways of trying (...) to remedy this situation: [i] redesigning the epistemic tool to improve the epistemic virtues of memory, perception, and attention; and [ii] the cultivation of cognitive diligence for wayfinding tasks scaffolding intellectual autonomy and carefulness. (shrink)

The value of optimality modeling has long been a source of contention amongst population biologists. Here I present a view of the optimality approach as at once playing a crucial explanatory role and yet also depending on external sources of confirmation. Optimality models are not alone in facing this tension between their explanatory value and their dependence on other approaches; I suspect that the scenario is quite common in science. This investigation of the optimality approach thus serves as a case (...) study, on the basis of which I suggest that there is a widely felt tension in science between explanatory independence and broad epistemic interdependence, and that this tension influences scientific methodology. (shrink)

Since anonymous agents can spread misinformation with impunity, many people advocate for greater accountability for internet speech. This paper provides a veritistic argument that accountability mechanisms can cause significant epistemic problems for internet encyclopedias and social media communities. I show that accountability mechanisms can undermine both the dissemination of true beliefs and the detection of error. Drawing on social psychology and behavioral economics, I suggest alternative mechanisms for increasing the trustworthiness of internet communication.