Colin Klein

Rapid progress in artificial intelligence (AI) capabilities has drawn fresh attention to the prospect of consciousness in AI. There is an urgent need for rigorous methods to assess AI systems for consciousness, but significant uncertainty about relevant issues in consciousness science. We present a method for assessing AI systems for consciousness that involves exploring what follows from existing or future neuroscientific theories of consciousness. Indicators derived from such theories can be used to inform credences about whether particular AI systems are conscious. This method allows us to make meaningful progress because some influential theories of consciousness, notably including computational functionalist theories, have implications for AI that can be investigated empirically.

A new way to transpose the virtue epistemologist’s ‘knowledge = apt belief’ template to the collective level, as a thesis about group knowledge, is developed. In particular, it is shown how specifically judgmental belief can be realised at the collective level in a way that is structurally analogous, on a telic theory of epistemic normativity (e.g., Sosa 2020), to how it is realised at the individual level—viz., through a (collective) intentional attempt to get it right aptly (whether p) by alethically affirming that p. An advantage of the proposal developed is that it is shown to be compatible with competing views—viz., joint acceptance accounts and social-distributive accounts—of how group members must interact in order to materially realise a group belief. I conclude by showing how the proposed judgment-focused collective (telic) virtue epistemology has important advantages over a rival version of collective virtue epistemology defended in recent work by Jesper Kallestrup (2016).

A new way to transpose the virtue epistemologist’s ‘knowledge = apt belief’ template to the collective level, as a thesis about group knowledge, is developed. In particular, it is shown how specifically judgmental belief can be realised at the collective level in a way that is structurally analogous, on a telic theory of epistemic normativity (e.g., Sosa 2020), to how it is realised at the individual level—viz., through a (collective) intentional attempt to get it right aptly (whether p) by alethically affirming that p. An advantage of the proposal developed is that it is shown to be compatible with competing views—viz., joint acceptance accounts and social-distributive accounts—of how group members must interact in order to materially realise a group belief. I conclude by showing how the proposed judgment-focused collective (telic) virtue epistemology has important advantages over a rival version of collective virtue epistemology defended in recent work by Jesper Kallestrup (2016).

An adequate theory of representation should distinguish between the structure of a representation and the structure of what it represents. I argue that the simplest sorts of transformers (the architecture that underlies most familiar Large Language Models) have only a very lightweight structure for their representations: insofar as they work with the structure of language, they represent it but do not use it. In addition to being interesting in its own right, this also shows how we may use high-level invariants at the computational level to place constraints on representational formats at the algorithmic level.

A pair of arguments for the indeterminacy of physical logic gates play an important role in debates about computational individuation. These arguments purport to show that one needs extrinsic, contextual factors to individuate even simple computations. One of the arguments has a straightforward flaw. Reflection on that flaw shows that there is a (mostly tacit) assumption on both sides of the debate: that computations ought to be understood in a function-theoretic way. I describe an alternative structure-theoretic understanding of the intrinsic mathematical structures instantiated by computations. I show that on a structure-­theoretic account, there is no indeterminacy. Instead, any logic gate belongs to a fully determinate equivalence class under isomorphism. Some independent advantages of a structure-theoretic account are also noted.

Generative artificial intelligence systems based on transformers, including both text-generators like GPT-4 and image generators like DALL-E 3, have recently entered the popular consciousness. These tools, while impressive, are liable to reproduce, exacerbate, and reinforce extant human social biases, such as gender and racial biases. In this paper, we systematically review the extent to which DALL-E Mini suffers from this problem. In line with the Model Card published alongside DALL-E Mini by its creators, we find that the images it produces tend to represent dozens of different occupations as populated either solely by men (e.g., pilot, builder, plumber) or solely by women (e.g., hairdresser, receptionist, dietitian). In addition, the images DALL-E Mini produces tend to represent most occupations as populated primarily or solely by White people (e.g., farmer, painter, prison officer, software engineer) and very few by non-White people (e.g., pastor, rapper). These findings suggest that exciting new AI technologies should be critically scrutinized and perhaps regulated before they are unleashed on society.

In a target article that appeared in this journal, Thomas Stoffregen 2000 questions the possibility of ecological event perception research. This paper describes an experiments performed to examine the perception of the disappearance of gap-crossing affordances, a variety of event as defined by Chemero 2000. We found that subjects reliably perceive both gap-crossing affordances and the disappearance of gap-crossing affordances. Our findings provide empirical evidence in favor of understanding events as changes in the layout of affordances, shoring up event perception research in ecological psychology.

In this chapter, we explore the prospects of epistemic minimax and related principles. Minimax is a well-known approach to choosing a strategy under conditions of risk and uncertainty. In individual cases, the minimax strategy selects the action that can lead to the least bad outcome for the agent, even if taking that action ensures that expected utility is not maximized and that best-case outcomes are impossible. In social cases, the minimax strategy selects the policy that maximizes the wellbeing of the least-well-off members of the group, even if doing so ensures that the aggregate utility of the group is not maximized and that best-case social outcomes are impossible. While there are many criticisms of minimax at both the individual and the social level, it is a prominent and popular approach at the social level because it prioritizes those at the bottom of the ladder. This chapter focuses on the social case for epistemic minimax and related principles. We develop the notions of epistemic position and epistemic power, both of which are prima facie epistemic goods, and demonstrate that a minimax principle can come apart from principles that would promote aggregate or maximum epistemic position or power. We also point to empirical and computational work that shows that testimonial networks online tend to be characterized by extreme inequalities of epistemic power. Then we extend traditional arguments in favor of social minimax to show that they hold in the epistemic case as well. In light of these arguments, we contend that the epistemic minimax principle should be treated as a heuristic for network interventions in democratic communities, and that it may be attractive for other types of communities as well. We conclude by examining the idea that some communities—especially the scientific community—should probably adopt different principles. Communities vary along different dimensions, and these dimensions may favor or disfavor different epistemic policies and principles, showing the sensitivity of our arguments to particular circumstances.

Trevor Griffith and Adrian Kind argue that we should reject a standard interpretation of pain asymbolia, according to which asymbolics experience pain even though their pain lacks the affective-motivational element that typical pains possess. We make the case that Griffith and Kind’s reasons for rejecting the standard interpretation are relatively weak. We end by arguing that debates between the standard interpretation and alternative interpretations cannot be resolved without addressing the issue of how we should taxonomize pain asymbolia as a neurological condition.

Pains are subject to obvious, well-documented, and striking top-down influences. This is in stark contrast to visual perception, where the debate over cognitive penetrability tends to revolve around fairly subtle experimental effects. Several authors have recently taken up the question of whether top-down effects on pain count as cognitive penetrability, and what that might show us about traditional debates. I review some of the known mechanisms for top-down modulation of pain, and suggest that it reveals an issue with a relatively neglected part of the cognitive penetrability literature. Much of the debate inherits Pylyshyn’s stark contrast between transducers and cognition proper. His distinction grew out of his running fight with Gibson, and is far too strong to be defensible. I suggest that we might therefore view top-down influences on pain as a species of transducer calibration. This provides a novel but principled way to distinguish between several varieties of top-down effect according to their architectural features.

Nikola Grahek's influential book Feeling Pain and Being in Pain introduced philosophers to the strange phenomenon of pain asymbolia. Subsequent philosophical debate around asymbolia has been partly taxonomic: the deep question is whether it is best understood as a specific neurological deficit or part of a broader syndrome. This paper looks to the history of asymbolia, positioning the origin of the term within broader historical trends. It shows that strange phenomena about pain and motivation have always presented interpretive challenges, and suggests that the current debate mirrors a historical split between German and French traditions. This does not resolve the debate, but does help place it within broader scientific and philosophical contexts.

Computers are used to make decisions in an increasing number of domains. There is widespread agreement that some of these uses are ethically problematic. Far less clear is where ethical problems arise, and what might be done about them. This paper expands and defends the Ethical Gravity Thesis: ethical problems that arise at higher levels of analysis of an automated decision-making system are inherited by lower levels of analysis. Particular instantiations of systems can add new problems, but not ameliorate more general ones. We defend this thesis by adapting Marr’s famous 1982 framework for understanding information-processing systems. We show how this framework allows one to situate ethical problems at the appropriate level of abstraction, which in turn can be used to target appropriate interventions.

Pains are subject to obvious, well-documented, and striking top-down influences. This is in stark contrast to visual perception, where the debate over cognitive penetrability tends to revolve around fairly subtle experimental effects. Several authors have recently taken up the question of whether top-down effects on pain count as cognitive penetrability, and what that might show us about traditional debates. I review some of the known mechanisms for top-down modulation of pain, and suggest that it reveals an issue with a relatively neglected part of the cognitive penetrability literature. Much of the debate inherits Pylyshyn’s stark contrast between transducers and cognition proper. His distinction grew out of his running fight with Gibson, and is far too strong to be defensible. I suggest that we might therefore view top-down influences on pain as a species of transducer calibration. This provides a novel but principled way to distinguish between several varieties of top-down effect according to their architectural features.

In this paper we analyses the work of the first century Chinese philosopher Wang Chong as in part grappling with epistemology of testimony. Often portrayed as a curmudgeonly skeptic, Wang Chong actually best seen as a demanding piecemeal non-reductionist, which is to say he believed that testimony was a basic source of evidence unless subject to a defeater (non-reductionism), but also that we should evaluate testimony on a claim-by-claim basis (piecemeal) rather than accepting a whole source on the strength of its reputation or authority, and finally he thought one has a strong duty to search for defeaters (demanding). We defend each of these claims as well as the fruitfulness of applying such anachronistic terminology to an ancient thinker. We end with a discussion of how Wang Chong's epistemology relates to his rhetorical method and then consider some problems he faces because of his epistemic commitments.

At present, the science of consciousness is structured around the search for the neural correlates of consciousness (the NCCs). One of the alleged advantages of the NCCs framework is its metaphysical neutrality—the fact that it begs no contested questions with respect to debates about the fundamental nature of consciousness. Here, we argue that even if the NCC framework is metaphysically neutral, it is structurally committed, for it presupposes a certain model—what we call the Lite-Brite model—of consciousness. This, we argue, represents a serious liability for the NCC framework for the plausibility of the Lite-Brite model is very much an open question, and the science of consciousness would be better served by a framework that does not presuppose it. Drawing on interventionist ideas in the philosophy of science, we suggest that the Difference-Maker framework can provide just such an alternative. Instead of searching for the neural correlates of consciousness (NCCs), we ought to be searching for the difference makers of consciousness (DMCs). We detail how a shift to searching DMCs will change both the practice of consciousness science and the interpretation of existing results.

Conspiracy theories play a troubling role in political discourse. Online forums provide a valuable window into everyday conspiracy theorizing, and can give a clue to the motivations and interests of those who post in such forums. Yet this online activity can be difficult to quantify and study. We describe a unique approach to studying online conspiracy theorists which used non-negative matrix factorization to create a topic model of authors' contributions to the main conspiracy forum on Reddit. This subreddit provides a large corpus of comments which spans many years and numerous authors. We show that within the forum, there are multiple sub-populations distinguishable by their loadings on different topics in the model. Further, we argue, these differences are interpretable as differences in background beliefs and motivations. The diversity of the distinct subgroups places constraints on theories of what generates conspiracy theorizing. We argue that traditional “monological” believers are only the tip of an iceberg of commenters. Neither simple irrationality nor common preoccupations can account for the observed diversity. Instead, we suggest, those who endorse conspiracies seem to be primarily brought together by epistemological concerns, and that these central concerns link an otherwise heterogenous group of individuals.

The medium‐independence of computational descriptions has shaped common conceptions of computational explanation. So long as our goal is to explain how a system successfully carries out its computations, then we only need to describe the abstract series of operations that achieve the desired input–output mapping, however they may be implemented. It is argued that this abstract conception of computational explanation cannot be applied to so‐called real‐time computing systems, in which meeting temporal deadlines imposed by the systems with which a device interfaces are constitutive of the computing tasks that a device performs. Instead, real‐time computing reveals the need for alternative conceptions of computational explanation, as well as computational implementation, that eschew medium‐independence.