Moral encroachment holds that the epistemic justification of a belief can be affected by moral factors. If the belief might wrong a person or group more evidence is required to justify the belief. Moral encroachment thereby opposes evidentialism, and kindred views, which holds that epistemic justification is determined solely by factors pertaining to evidence and truth. In this essay I explain how beliefs such as ‘that woman is probably an administrative assistant’—based on the evidence that most women employees at the (...) firm are administrative assistants—motivate moral encroachment. I then describe weaknesses of moral encroachment. Finally I explain how we can countenance the moral properties of such beliefs without endorsing moral encroachment, and I argue that the moral status of such beliefs cannot be evaluated independently from the understanding in which they are embedded. (shrink)

The inaugural title in the new, Open Access series BSPS Open, The Material Theory of Induction will initiate a new tradition in the analysis of inductive inference. The fundamental burden of a theory of inductive inference is to determine which are the good inductive inferences or relations of inductive support and why it is that they are so. The traditional approach is modeled on that taken in accounts of deductive inference. It seeks universally applicable schemas or rules or a single (...) formal device, such as the probability calculus. After millennia of halting efforts, none of these approaches has been unequivocally successful and debates between approaches persist. The Material Theory of Induction identifies the source of these enduring problems in the assumption taken at the outset: that inductive inference can be accommodated by a single formal account with universal applicability. Instead, it argues that that there is no single, universally applicable formal account. Rather, each domain has an inductive logic native to it. Which that is, and its extent, is determined by the facts prevailing in that domain. Paying close attention to how inductive inference is conducted in science and copiously illustrated with real-world examples, The Material Theory of Induction will initiate a new tradition in the analysis of inductive inference. (shrink)

A scientific community cannot practice its trade without some set of received beliefs. These beliefs form the foundation of the "educational initiation that prepares and licenses the student for professional practice". The nature of the "rigorous and rigid" preparation helps ensure that the received beliefs are firmly fixed in the student's mind. Scientists take great pains to defend the assumption that scientists know what the world is like...To this end, "normal science" will often suppress novelties which undermine its foundations. Research (...) is therefore not about discovering the unknown, but rather "a strenuous and devoted attempt to force nature into the conceptual boxes supplied by professional education". (shrink)

Thomas S. Kuhn's classic book is now available with a new index.

In his new foreword to this edition, Hilary Putnam forcefully rejects these nativist claims.

Traditional philosophical discussions of knowledge have focused on the epistemic status of full beliefs. In this book, Moss argues that in addition to full beliefs, credences can constitute knowledge. For instance, your .4 credence that it is raining outside can constitute knowledge, in just the same way that your full beliefs can. In addition, you can know that it might be raining, and that if it is raining then it is probably cloudy, where this knowledge is not knowledge of propositions, (...) but of probabilistic contents. -/- The notion of probabilistic content introduced in this book plays a central role not only in epistemology, but in the philosophy of mind and language as well. Just as tradition holds that you believe and assert propositions, you can believe and assert probabilistic contents. Accepting that we can believe, assert, and know probabilistic contents has significant consequences for many philosophical debates, including debates about the relationship between full belief and credence, the semantics of epistemic modals and conditionals, the contents of perceptual experience, peer disagreement, pragmatic encroachment, perceptual dogmatism, and transformative experience. In addition, accepting probabilistic knowledge can help us discredit negative evaluations of female speech, explain why merely statistical evidence is insufficient for legal proof, and identify epistemic norms violated by acts of racial profiling. Hence the central theses of this book not only help us better understand the nature of our own mental states, but also help us better understand the nature of our responsibilities to each other. (shrink)

Philosophers of science increasingly recognize the importance of idealization: the intentional introduction of distortion into scientific theories. Yet this recognition has not yielded consensus about the nature of idealization. e literature of the past thirty years contains disparate characterizations and justifications, but little evidence of convergence towards a common position.

Philosophical work on values in science is held back by widespread ambiguity about how values bear on sci entific choices. Here, I disambiguate several ways in which a choice can be value-laden and show that this disambiguation has the potential to solve and dissolve philosophical problems about values in science. First, I characterize four ways in which values relate to choices: values can motivate, justify, cause, or be impacted by the choices we make. Next, I put my proposed taxonomy to (...) work, using it to clarify one version of the argument from inductive risk. The claim that non-epistemic values must play a role in scientific choices that run inductive risk makes most sense as a claim about values being needed to justify such choices. The argument from inductive risk is not unique: many philosophical arguments about values in science can be more clearly understood and assessed by paying close attention to how values and choices are related. (shrink)

Jason Stanley presents a startling and provocative claim about knowledge: that whether or not someone knows a proposition at a given time is in part determined by his or her practical interests, i.e. by how much is at stake for that person at that time. In defending this thesis, Stanley introduces readers to a number of strategies for resolving philosophical paradox, making the book essential not just for specialists in epistemology but for all philosophers interested in philosophical methodology. Since a (...) number of his strategies appeal to linguistic evidence, it will be of great interest to linguists as well. (shrink)

Jason Stanley's "Knowledge and Practical Interests" is a brilliant book, combining insights about knowledge with a careful examination of how recent views in epistemology fit with the best of recent linguistic semantics. Although I am largely convinced by Stanley's objections to epistemic contextualism, I will try in what follows to formulate a version that might have some prospect of escaping his powerful critique.

To understand one another as individuals and to fulfill the moral duties that require such understanding, we must communicate with each other. We must also maintain protected channels that render reliable communication possible, a demand that, Seana Shiffrin argues, yields a prohibition against lying and requires protection for free speech. This book makes a distinctive philosophical argument for the wrong of the lie and provides an original account of its difference from the wrong of deception. Drawing on legal as well (...) as philosophical arguments, the book defends a series of notable claims—that you may not lie about everything to the "murderer at the door," that you have reasons to keep promises offered under duress, that lies are not protected by free speech, that police subvert their mission when they lie to suspects, and that scholars undermine their goals when they lie to research subjects. Many philosophers start to craft moral exceptions to demands for sincerity and fidelity when they confront wrongdoers, the pressures of non-ideal circumstances, or the achievement of morally substantial ends. But Shiffrin consistently resists this sort of exceptionalism, arguing that maintaining a strong basis for trust and reliable communication through practices of sincerity, fidelity, and respecting free speech is an essential aspect of ensuring the conditions for moral progress, including our rehabilitation of and moral reconciliation with wrongdoers. (shrink)

The question of the relationship of the making of value judgments in a typically ethical sense to the methods and procedures of science has been discussed in the literature at least to that point which e. e. cummings somewhere refers to as “The Mystical Moment of Dullness.” Nevertheless, albeit with some trepidation, I feel that something more may fruitfully be said on the subject.

We call attention to an underappreciated way in which non-epistemic values influence evidence evaluation in science. Our argument draws upon some well-known features of scientific modeling. We show that, when scientific models stand in for background knowledge in Bayesian and other probabilistic methods for evidence evaluation, conclusions can be influenced by the non-epistemic values that shaped the setting of priorities in model development. Moreover, it is often infeasible to correct for this influence. We further suggest that, while this value influence (...) is not particularly prone to the problem of wishful thinking, it could have problematic non-epistemic consequences in some cases. (shrink)

Table of Contents -/- 1. Introduction and Overview: Two Entitlement Projects, Peter J. Graham, Nikolaj J.L.L. Pedersen, Zachary Bachman, and Luis Rosa -/- Part I. Engaging Burge's Project -/- 2. Entitlement: The Basis of Empirical Warrant, Tyler Burge 3. Perceptual Entitlement and Scepticism, Anthony Brueckner and Jon Altschul 4. Epistemic Entitlement Its Scope and Limits, Mikkel Gerken 5. Why Should Warrant Persist in Demon Worlds?, Peter J. Graham -/- Part II. Extending the Externalist Project -/- 6. Epistemic Entitlement and Epistemic (...) Competence, Ernest Sosa 7. Extended Entitlement, Adam Carter and Duncan Pritchard 8. Moorean Pragmatics, Social Comparisons and Common Knowledge, Allan Hazlett 9. Internalism and Entitlement to Rules and Methods, Joshua Schecter -/- Part III. Engaging Wright's Project -/- 10. Full Bloodied Entitlement, Martin Smith 11. Pluralist Consequentialist Anti-Scepticism, Nikolaj Jang Lee Linding Pedersen 12. Against (Neo-Wittensteinian) Entitlements, Annalisa Coliva 13. The Truth Fairy and the Indirect Consequentialist, Daniel Elstein and Carrie S. I. Jenkins 14. Knowledge for Nothing, Patrick Greenough . (shrink)

Contrary to formal theories of induction, I argue that there are no universal inductive inference schemas. The inductive inferences of science are grounded in matters of fact that hold only in particular domains, so that all inductive inference is local. Some are so localized as to defy familiar characterization. Since inductive inference schemas are underwritten by facts, we can assess and control the inductive risk taken in an induction by investigating the warrant for its underwriting facts. In learning more facts, (...) we extend our inductive reach by supplying more localized inductive inference schemes. Since a material theory no longer separates the factual and schematic parts of an induction, it proves not to be vulnerable to Hume's problem of the justification of induction. (shrink)

This essay warns of eroding accountability in computerized societies. It argues that assumptions about computing and features of situations in which computers are produced create barriers to accountability. Drawing on philosophical analyses of moral blame and responsibility, four barriers are identified: 1) the problem of many hands, 2) the problem of bugs, 3) blaming the computer, and 4) software ownership without liability. The paper concludes with ideas on how to reverse this trend.

Visual perception relies on stored information and environmental associations to arrive at a determinate representation of the world. This opens up the disturbing possibility that our visual experiences could themselves be subject to a kind of racial bias, simply in virtue of accurately encoding previously encountered environmental regularities. This possibility raises the following question: what, if anything, is wrong with beliefs grounded upon these prejudicial experiences? They are consistent with a range of epistemic norms, including evidentialist and reliabilist standards for (...) justification. I argue that we will struggle to locate a flaw with these sorts of perceptual beliefs so long as we focus our analysis at the level of the individual and her response to information. We should instead broaden our analysis to include the social structure within which the individual is located. Doing so lets us identify a problem with the way in which unjust social structures in particular “gerrymander” the regularities an individual is exposed to, and by extension the priors their visual system draws on. I argue that in this way, social structures can cap perceptual skill. (shrink)

What, if anything, is epistemically wrong with beliefs involving accurate statistical generalizations about demographic groups? This paper argues that there is a perfectly general, underappreciated epistemic flaw which affects both ethically charged and uncharged statistical generalizations. Though common to both, this flaw can also explain why demographic statistical generalizations give rise to the concerns they do. To identify this flaw, we need to distinguish between the accuracy and the projectability of statistical beliefs. Statistical beliefs are accompanied by an implicit representation (...) of the statistic's modal profile. Their modal profile determines the circumstances in which they can legitimately be projected to unobserved instances. Errors in that implicit content can be compatible with the accuracy of the “bare” statistic, whilst systematically leading to downstream errors in reasoning, in a manner which reveals an epistemic flaw with an important aspect of the belief state itself. (shrink)

We argue, contrary to epistemological orthodoxy, that knowledge is not purely epistemic—that knowledge is not simply a matter of truth‐related factors (evidence, reliability, etc.). We do this by arguing for a pragmatic condition on knowledge, KA: if a subject knows that p, then she is rational to act as if p. KA, together with fallibilism, entails that knowledge is not purely epistemic. We support KA by appealing to the role of knowledge‐citations in defending and criticizing actions, and by giving a (...) principled argument for KA, based on the inference rule KB: if a subject knows that A is the best thing she can do, she is rational to do A. In the second half of the paper, we consider and reject the two most promising objections to our case for KA, one based on the Gricean notion of conversational implicature and the other based on a contextualist maneuver. (shrink)

Traits like simplicity and explanatory power have traditionally been treated as values internal to the sciences, constitutive rather than contextual. As such they are cognitive virtues. This essay contrasts a traditional set of such virtues with a set of alternative virtues drawn from feminist writings about the sciences. In certain theoretical contexts, the only reasons for preferring a traditional or an alternative virtue are socio-political. This undermines the notion that the traditional virtues can be considered purely cognitive.

There are at least twelve major virtues of good theories: evidential accuracy, causal adequacy, explanatory depth, internal consistency, internal coherence, universal coherence, beauty, simplicity, unification, durability, fruitfulness, and applicability. These virtues are best classified into four classes: evidential, coherential, aesthetic, and diachronic. Each virtue class contains at least three virtues that sequentially follow a repeating pattern of progressive disclosure and expansion. Systematizing the theoretical virtues in this manner clarifies each virtue and suggests how they might have a coordinated and cumulative (...) role in theory formation and evaluation across the disciplines—with allowance for discipline specific modification. An informal and flexible logic of theory choice is in the making here. Evidential accuracy, according to my systematization, is not a largely isolated trait of good theories, as some have made it out to be. Rather, it bears multifaceted relationships, constituting significant epistemic entanglements, with other theoretical virtues. (shrink)

What is a bias? Standard philosophical views of both implicit and explicit bias focus this question on the representations one harbours, for example, stereotypes or implicit attitudes, rather than the ways in which those representations are manipulated. I call this approach representationalism. In this paper, I argue that representationalism taken as a general theory of psychological social bias is a mistake, because it conceptualizes bias in ways that do not fully capture the phenomenon. Crucially, this view fails to capture a (...) heretofore neglected possibility of bias, one that influences an individual’s beliefs about or actions toward others, but is, nevertheless, nowhere represented in that individual’s cognitive repertoire. In place of representationalism, I develop a functional account of psychological social bias which characterizes it as a mental entity that takes propositional mental states as inputs and returns propositional mental states as outputs in a way that instantiates social-kind inductions. This functional characterization leaves open which mental states and processes bridge the gap between the inputs and outputs, ultimately highlighting the diversity of candidates that can serve this role. (shrink)

Often machine learning programs inherit social patterns reflected in their training data without any directed effort by programmers to include such biases. Computer scientists call this algorithmic bias. This paper explores the relationship between machine bias and human cognitive bias. In it, I argue similarities between algorithmic and cognitive biases indicate a disconcerting sense in which sources of bias emerge out of seemingly innocuous patterns of information processing. The emergent nature of this bias obscures the existence of the bias itself, (...) making it difficult to identify, mitigate, or evaluate using standard resources in epistemology and ethics. I demonstrate these points in the case of mitigation techniques by presenting what I call ‘the Proxy Problem’. One reason biases resist revision is that they rely on proxy attributes, seemingly innocuous attributes that correlate with socially-sensitive attributes, serving as proxies for the socially-sensitive attributes themselves. I argue that in both human and algorithmic domains, this problem presents a common dilemma for mitigation: attempts to discourage reliance on proxy attributes risk a tradeoff with judgement accuracy. This problem, I contend, admits of no purely algorithmic solution. (shrink)

What is the best account of process reliabilism about epistemic justification, especially epistemic entitlement? I argue that entitlement consists in the normal functioning (proper operation) of the belief-forming process when the process has forming true beliefs reliably as an etiological function. Etiological functions involve consequence explanation: a belief-forming process has forming true beliefs reliably as a function just in case forming-true beliefs reliably partly explains the persistence of the process. This account paves the way for avoiding standard objections to process (...) reliabilism and situations epistemic entitlement within a normative framework of functions and functional norms. (shrink)

Measures of epistemic utility are used by formal epistemologists to make determinations of epistemic betterness among cognitive states. The Brier rule is the most popular choice among formal epistemologists for such a measure. In this paper, however, we show that the Brier rule is sometimes seriously wrong about whether one cognitive state is epistemically better than another. In particular, there are cases where an agent gets evidence that definitively eliminates a false hypothesis, but where the Brier rule says that things (...) have become epistemically worse. Along the way to this ‘elimination experiment’ counter-example to the Brier rule as a measure of epistemic utility, we identify several useful monotonicity principles for epistemic betterness. We also reply to several potential objections to this counter-example. (shrink)

The moral terrain of science, the full range of ethical considerations that are part of the scientific endeavor, has not been mapped. Without such a map, we cannot examine the responsibilities of scientists to see if the institutions of science are adequately constructed. This paper attempts such a map by describing four dimensions of the terrain: (1) the bases to which scientists are responsible (scientific reasoning, the scientific community, and the broader society); (2) the nature of the responsibility (general or (...) role); (3) the level of responsibility (minimum demand or ideal); and (4) who bears the responsibility (the individual or the community). Such a map will be used to elucidate the recent debate over the publication of studies concerning H5N1 flu virus. (shrink)

Although epistemic values have become widely accepted as part of scientific reasoning, non-epistemic values have been largely relegated to the "external" parts of science (the selection of hypotheses, restrictions on methodologies, and the use of scientific technologies). I argue that because of inductive risk, or the risk of error, non-epistemic values are required in science wherever non-epistemic consequences of error should be considered. I use examples from dioxin studies to illustrate how non-epistemic consequences of error can and should be considered (...) in the internal stages of science: choice of methodology, characterization of data, and interpretation of results. (shrink)

I use a theorem from machine learning, called the “No Free Lunch” theorem to support the claim that non-epistemic values are essential to theory choice. I argue that NFL entails that predictive accuracy is insufficient to favor a given theory over others, and that NFL challenges our ability to give a purely epistemic justification for using other traditional epistemic virtues in theory choice. In addition, I argue that the natural way to overcome NFL’s challenge is to use non-epistemic values. If (...) my argument holds, non-epistemic values are entangled in theory choice regardless of human limitations and regardless of the subject matter. Thereby, my argument overcomes objections to the main lines of argument revealing the role of values in theory choice. At the end of the paper, I argue that, contrary to common conception, the epistemic challenge arising from NFL is distinct from Hume’s problem of induction and other forms of underdetermination. (shrink)

Big Data evangelists often argue that algorithms make decision-making more informed and objective—a promise hotly contested by critics of these technologies. Yet, to date, most of the debate has focused on the instruments themselves, rather than on how they are used. This article addresses this lack by examining the actual practices surrounding algorithmic technologies. Specifically, drawing on multi-sited ethnographic data, I compare how algorithms are used and interpreted in two institutional contexts with markedly different characteristics: web journalism and criminal justice. (...) I find that there are surprising similarities in how web journalists and legal professionals use algorithms in their work. In both cases, I document a gap between the intended and actual effects of algorithms—a process I analyze as “decoupling.” Second, I identify a gamut of buffering strategies used by both web journalists and legal professionals to minimize the impact of algorithms in their daily work. Those include foot-dragging, gaming, and open critique. Of course, these similarities do not exhaust the differences between the two cases, which are explored in the discussion section. I conclude with a call for further ethnographic work on algorithms in practice as an important empirical check against the dominant rhetoric of algorithmic power. (shrink)

Data-driven, decision-making technologies used in the justice system to inform decisions about bail, parole, and prison sentencing are biased against historically marginalized groups (Angwin, Larson, Mattu, & Kirchner 2016). But these technologies’ judgments—which reproduce patterns of wrongful discrimination embedded in the historical datasets that they are trained on—are well-evidenced. This presents a puzzle: how can we account for the wrong these judgments engender without also indicting morally permissible statistical inferences about persons? I motivate this puzzle and attempt an answer.

I argue that inferences from highly probabilifying racial generalizations are not solely objectionable because acting on such inferences would be problematic, or they violate a moral norm, but because they violate a distinctively epistemic norm. They involve accepting a proposition when, given the costs of a mistake, one is not adequately justified in doing so. First I sketch an account of the nature of adequate justification—practical adequacy with respect to eliminating the ~p possibilities from one’s epistemic statespace. Second, I argue (...) that inferences based on demographic generalizations tend to disproportionately expose group members to the risks associated with mistakenly assuming stereotypical propositions, and so magnify the wrong involved in relying on such inferences without adequate justification. (shrink)

This paper is about an overlooked aspect—the cognitive or epistemic aspect—of the moral demand we place on one another to be treated well. We care not only how people act towards us and what they say of us, but also what they believe of us. That we can feel hurt by what others believe of us suggests both that beliefs can wrong and that there is something we epistemically owe to each other. This proposal, however, surprises many theorists who claim (...) it lacks both intuitive and theoretical support. This paper argues that the proposal has intuitive support and is not at odds with much contemporary theorizing about what we owe to each other. (shrink)

We care not only about how people treat us, but also what they believe of us. If I believe that you’re a bad tipper given your race, I’ve wronged you. But, what if you are a bad tipper? It is commonly argued that the way racist beliefs wrong is that the racist believer either misrepresents reality, organizes facts in a misleading way that distorts the truth, or engages in fallacious reasoning. In this paper, I present a case that challenges this (...) orthodoxy: the case of the supposedly rational racist. We live in a world that has been, and continues to be, structured by racist attitudes and institutions. As a result, the evidence might be stacked in favour of racist beliefs. But, if there are racist beliefs that reflect reality and are rationally justified, what could be wrong with them? Moreover, how do I wrong you by believing what I epistemically ought believe given the evidence? To address this challenge, we must recognize that there are not only epistemic norms governing belief, but moral ones as well. This view, however, is at odds with the assumption that moral obligation requires a kind of voluntary control that we lack with regard to our beliefs. This background assumption motivates many philosophers to try to explain away the appearance that beliefs can wrong by locating the wrong elsewhere, e.g., in an agent’s actions. Further, even accounts that accept the thesis that racist beliefs can wrong restrict the class of beliefs that wrong to beliefs that are either false or the result of hot irrationality, e.g., the racist belief is a result of ill-will. In this paper I argue that although the these accounts will capture many of the wrongs associated with racist beliefs, they will be only partial explanations because they cannot explain the wrong committed by the supposedly rational racist. The challenge posed by the supposedly rational racist concerns our epistemic practices in a non-ideal world. The world is an unjust place, and there may be many morally objectionable beliefs it justifies. To address this challenge, we must seriously consider the thesis people wrong others in virtue of what they believe about them, and not just in virtue of what they do. (shrink)

Perhaps this is due to the belief that technology has a value-neutral nature, and that issues of value are better left to philosophers.

Douglas proposes a new ideal in which values serve an essential function throughout scientific inquiry, but where the role values play is constrained at key points, protecting the integrity and objectivity of science.

No-free-lunch theorems are important theoretical result in the fields of machine learning and artificial intelligence. Researchers in this fields often claim that the theorems are based on Hume’s argument about induction and represent a formalisation of the argument. This paper argues that this is erroneous but that the theorems correspond to and formalise Goodman’s new riddle of induction. To demonstrate the correspondence among the theorems and Goodman’s argument, a formalisation of the latter in the spirit of the former is sketched.