Reliabilism, Misc

We can infer moral conclusions from nonmoral evidence using a three-step procedure. First, we distinguish the processes generating belief so that their reliability in generating true belief is statistically predictable. Second, we assess the processes’ reliability, perhaps by observing how frequently they generate true nonmoral belief or logically inconsistent beliefs. Third, we adjust our credence in moral propositions in light of the truth ratios of the processes generating beliefs in them. This inferential route involves empirically discovering truths of the form (...) “Process P, which generates belief in moral proposition M, has truth ratio T", and using them to discover probabilities for moral propositions. The inferential route is noncircular, and progress along it is driven fundamentally by induction. (shrink)

This paper aims to show that Selim Berker’s widely discussed prime number case is merely an instance of the well-known generality problem for process reliabilism and thus arguably not as interesting a case as one might have thought. Initially, Berker’s case is introduced and interpreted. Then the most recent response to the case from the literature is presented. Eventually, it is argued that Berker’s case is nothing but a straightforward consequence of the generality problem, i.e., the problematic aspect of the (...) case for process reliabilism (if any) is already captured by the generality problem. (shrink)

Recently John Turri (2015b) has argued, contra the orthodoxy amongst epistemologists, that reliability is not a necessary condition for knowledge. From this result, Turri (2015a, 2017, 2016a, 2019) defends a new account of knowledge - called abilism - that allows for unreliable knowledge. I argue that Turri's arguments fail to establish that unreliable knowledge is possible and argue that Turri's account of knowledge is false because reliability must be a necessary condition for knowledge.

This is a survey article about reliabilism. It introduces its main ideas, outlines some of its core challenges and discusses a number of solutions.

The New Evil Demon Problem is meant to show that reliabilism about epistemic justification is incompatible with the intuitive idea that the external-world beliefs of a subject who is the victim of a Cartesian demon could be epistemically justified. Here, I present a new argument that such beliefs can be justified on reliabilism. Whereas others have argued for this conclusion by making some alterations in the formulation of reliabilism, I argue that, as far as the said problem is concerned, such (...) alterations are redundant. No reliabilist should fear the demon. (shrink)

Vice epistemology, as Quassim Cassam understands it, is the study of the nature, identity, and significance of the epistemic vices. But what makes an intellectual vice a vice? Cassam calls his own view “Obstructivism” – intellectual vices are those traits, thinking styles, or attitudes that systematically obstruct the acquisition, retention, and transmission of knowledge. -/- I shall argue that Cassam’s account is an improvement upon virtue-reliabilism, and that it fares better against what I call Montmarquet’s objection than its immediate rivals. (...) Nevertheless, I contend that it does not go far enough — Montmarquet’s objection stands. -/- I conclude that either the objection needs to be answered in some other way, or else proponents of Obstructivism need to explain why their account of the nature of the intellectual vices does not have the counterintuitive consequences it appears to have. Alternatively, another account of the nature of the intellectual vices needs to be sought. (shrink)

Goldman, though still a reliabilist, has made some recent concessions to evidentialist epistemologies. I agree that reliabilism is most plausible when it incorporates certain evidentialist elements, but I try to minimize the evidentialist component. I argue that fewer beliefs require evidence than Goldman thinks, that Goldman should construe evidential fit in process reliabilist terms, rather than the way he does, and that this process reliabilist understanding of evidence illuminates such important epistemological concepts as propositional justification, ex ante justification, and defeat.

Cognitive penetration of perception is the idea that what we see is influenced by such states as beliefs, expectations, and so on. A perceptual belief that results from cognitive penetration may be less justified than a nonpenetrated one. Inferentialism is a kind of internalist view that tries to account for this by claiming that some experiences are epistemically evaluable, on the basis of why the perceiver has that experience, and the familiar canons of good inference provide the appropriate standards by (...) which experiences are evaluated. I examine recent defenses of inferentialism by Susanna Siegel, Peter Markie, and Matthew McGrath and argue that the prospects for inferentialism are dim. (shrink)

Contemporary philosophers nearly unanimously endorse knowledge reliabilism, the view that knowledge must be reliably produced. Leading reliabilists have suggested that reliabilism draws support from patterns in ordinary judgments and intuitions about knowledge, luck, reliability, and counterfactuals. That is, they have suggested a proto-reliabilist hypothesis about “commonsense” or “folk” epistemology. This paper reports nine experimental studies (N = 1262) that test the proto-reliabilist hypothesis by testing four of its principal implications. The main findings are that (a) commonsense fully embraces the possibility (...) of unreliable knowledge, (b) knowledge judgments are surprisingly insensitive to information about reliability, (c) “anti-luck” intuitions about knowledge have nothing to do with reliability specifically, and (d) reliabilists have mischaracterized the intuitive counterfactual properties of knowledge and their relation to reliability. When combined with the weakness of existing arguments for reliabilism and the recent emergence of well supported alternative views that predict the widespread existence of unreliable knowledge, the present findings are the final exhibit in a conclusive case for abandoning reliabilism in epistemology. I introduce an alternative theory of knowledge, abilism, which out-performs reliabilism and well explains all the available evidence. (shrink)

According to epistemic internalism, the only facts that determine the justificational status of a belief are facts about the subject’s own mental states, like beliefs and experiences. Externalists instead hold that certain external facts, such as facts about the world or the reliability of a belief-producing mechanism, affect a belief’s justificational status. Some internalists argue that considerations about evil demon victims and brains in vats provide excellent reason to reject externalism: because these subjects are placed in epistemically unfavorable settings, externalism (...) seems unable to account for the strong intuition that these subjects’ beliefs are nonetheless justified. I think these considerations do not at all help the internalist cause. I argue that by appealing to the anti-individualistic nature of perception, it can be shown that skeptical scenarios provide no reason to prefer internalism to externalism. (shrink)

Is perception cognitively penetrable, and what are the epistemological consequences if it is? I address the latter of these two questions, partly by reference to recent work by Athanassios Raftopoulos and Susanna Seigel. Against the usual, circularity, readings of cognitive penetrability, I argue that cognitive penetration can be epistemically virtuous, when---and only when---it increases the reliability of perception.

Strategic Reliabilism is a framework that yields relative epistemic evaluations of belief-producing cognitive processes. It is a theory of cognitive excellence, or more colloquially, a theory of reasoning excellence (where 'reasoning' is understood very broadly as any sort of cognitive process for coming to judgments or beliefs). First introduced in our book, Epistemology and the Psychology of Human Judgment (henceforth EPHJ), the basic idea behind SR is that epistemically excellent reasoning is efficient reasoning that leads in a robustly reliable fashion (...) to significant, true beliefs. It differs from most contemporary epistemological theories in two ways. First, it is not a theory of justification or knowledge – a theory of epistemically worthy belief. Strategic Reliabilism is a theory of epistemically worthy ways of forming beliefs. And second, Strategic Reliabilism does not attempt to account for an epistemological property that is assumed to be faithfully reflected in the epistemic judgments and intuitions of philosophers. If SR makes recommendations that accord with our reflective epistemic judgments and intuitions, great. If not, then so much the worse for our reflective epistemic judgments and intuitions. (shrink)

Virtue reliabilism appears to have a major advantage over generic reliabilism: only the former has the resources to explain the intuition that knowledge is more valuable than mere true belief. I argue that this appearance is illusory. It is sustained only by the misguided assumption that a principled distinction can be drawn between those belief-forming methods that are grounded in the agent’s intellectual virtues, and those that are not. A further problem for virtue reliabilism is that of explaining why knowledge (...) is more valuable than mere justified true belief. I argue that virtue reliabilism lacks the resources to explain this value difference. I conclude by considering what it would take for a theory to explain the extra value of knowledge over mere justified true belief. (shrink)

This article asks the question, ``what is reliable machine learning?'' As I intend to answer it, this is a question about epistemic justification. Reliable machine learning gives justification for believing its output. Current approaches to reliability (e.g., transparency) involve showing the inner workings of an algorithm (functions, variables, etc.) and how they render outputs. We then have justification for believing the output because we know how it was computed. Thus, justification is contingent on what can be shown about the algorithm, (...) its properties, and its behavior. In this paper, I defend computational reliabilism (CR). CR is a computationally-inspired off-shoot of process reliabilism that does not require showing the inner workings of an algorithm. CR credits reliability to machine learning by identifying reliability indicators external to the algorithm (validation methods, knowledge-based integration, etc.). Thus, we have justification for believing the output of machine learning when we have identified the appropriate reliability indicators. CR is advanced as a more suitable epistemology for machine learning. The main goal of this article is to lay the groundwork for CR, how it works, and what we can expect as a justificatory framework for reliable machine learning. (shrink)