For synergistic interactions between humans and artificial intelligence (AI) systems, AI outputs often need to be explainable to people. Explainable AI (XAI) systems are commonly tested in human user studies. However, whether XAI researchers consider potential cultural differences in human explanatory needs remains unexplored. We highlight psychological research that found significant differences in human explanations between many people from Western, commonly individualist countries and people from non-Western, often collectivist countries. We argue that XAI research currently overlooks these variations and that (...) many popular XAI designs implicitly and problematically assume that Western explanatory needs are shared cross-culturally. Additionally, we systematically reviewed over 200 XAI user studies and found that most studies did not consider relevant cultural variations, sampled only Western populations, but drew conclusions about human-XAI interactions more generally. We also analyzed over 30 literature reviews of XAI studies. Most reviews did not mention cultural differences in explanatory needs or flag overly broad cross-cultural extrapolations of XAI user study results. Combined, our analyses provide evidence of a cultural bias toward Western populations in XAI research, highlighting an important knowledge gap regarding how culturally diverse users may respond to widely used XAI systems that future work can and should address. (shrink)

A growing interest in and concern about the adequacy and fairness of modern peer-review practices in publication and funding are apparent across a wide range of scientific disciplines. Although questions about reliability, accountability, reviewer bias, and competence have been raised, there has been very little direct research on these variables.The present investigation was an attempt to study the peer-review process directly, in the natural setting of actual journal referee evaluations of submitted manuscripts. As test materials we selected 12 already published (...) research articles by investigators from prestigious and highly productive American psychology departments, one article from each of 12 highly regarded and widely read American psychology journals with high rejection rates (80%) and nonblind refereeing practices.With fictitious names and institutions substituted for the original ones (e.g., Tri-Valley Center for Human Potential), the altered manuscripts were formally resubmitted to the journals that had originally refereed and published them 18 to 32 months earlier. Of the sample of 38 editors and reviewers, only three (8%) detected the resubmissions. This result allowed nine of the 12 articles to continue through the review process to receive an actual evaluation: eight of the nine were rejected. Sixteen of the 18 referees (89%) recommended against publication and the editors concurred. The grounds for rejection were in many cases described as “serious methodological flaws.” A number of possible interpretations of these data are reviewed and evaluated. (shrink)

Viral modernity is a concept based upon the nature of viruses, the ancient and critical role they play in evolution and culture, and the basic application to understanding the role of information and forms of bioinformation in the social world. The concept draws a close association between viral biology on the one hand, and information science on the other – it is an illustration and prime example of bioinformationalism that brings together two of the most powerful forces that now drive (...) cultural evolution. The concept of viral modernity applies to viral technologies, codes and ecosystems in information, publishing, education and emerging knowledge systems. This paper traces the relationship between epidemics, quarantine, and public health management and outlines elements of viral-digital philosophy based on the fusion of living and technological systems. We discuss Covid-19 as a ‘bioinformationalist’ response that represents historically unprecedented level of sharing information from the sequencing of the genome to testing for a vaccination. Finally, we look at the US response to Covid-19 through the lens of infodemics and post-truth. The paper is followed by three open reviews, which further refine its conclusions as they relate to philosophy and the notion of the virus as Pharmakon. (shrink)

Many scientists routinely generalize from study samples to larger populations. It is commonly assumed that this cognitive process of scientific induction is a voluntary inference in which researchers assess the generalizability of their data and then draw conclusions accordingly. We challenge this view and argue for a novel account. The account describes scientific induction as involving by default a generalization bias that operates automatically and frequently leads researchers to unintentionally generalize their findings without sufficient evidence. The result is unwarranted, overgeneralized (...) conclusions. We support this account of scientific induction by integrating a range of disparate findings from across the cognitive sciences that have until now not been connected to research on the nature of scientific induction. The view that scientific induction involves by default a generalization bias calls for a revision of the current thinking about scientific induction and highlights an overlooked cause of the replication crisis in the sciences. Commonly proposed interventions to tackle scientific overgeneralizations that may feed into this crisis need to be supplemented with cognitive debiasing strategies against generalization bias to most effectively improve science. (shrink)

Quantification is a topic which brings together linguistics, logic, and philosophy. Quantifiers are the essential tools with which, in language or logic, we refer to quantity of things or amount of stuff. In English they include such expressions as no, some, all, both, many. Peters and Westerstahl present the definitive interdisciplinary exploration of how they work - their syntax, semantics, and inferential role.

This paper is an experiment in collective writing conducted in Autumn 2019 at the Faculty of Education at Beijing Normal University. The experiment involves 12 international masters' students readi...

This article is concerned with developing a philosophical approach to a number of significant changes to academic publishing, and specifically the global journal knowledge system wrought by a range of new digital technologies that herald the third age of the journal as an electronic, interactive and mixed-media form of scientific communication. The paper emerges from an Editors' Collective, a small New Zealand-based organisation comprised of editors and reviewers of academic journals mostly in the fields of education and philosophy. The paper (...) is the result of a collective writing process. (shrink)

Demographic diversity might often be present in a group without group members noticing it. What are the epistemic effects if they do? Several philosophers and social scientists have recently argued that when individuals detect demographic diversity in their group, this can result in epistemic benefits even if that diversity doesn’t involve cognitive differences. Here I critically discuss research advocating this proposal, introduce a distinction between two types of detection of demographic diversity, and apply this distinction to the theorizing on diversity (...) in science. Focusing on ‘invisible’ diversity, I argue that in one common kind of group in science, if group members have full insight into their group’s diversity, this is likely to create epistemic costs. These costs can be avoided and epistemic benefits gained if group members only partly detect their group’s diversity. There is thus an epistemic reason for context-dependent limitations on scientists’ insight into the diversity of their group. (shrink)

Educational philosophies of self-cultivation as the foundation and cultural ethos for education have a strong and historically effective tradition stretching back to antiquity in the classical ‘cra...

In this book, Ted Peters explores the fallacies of the "gene myth" and presents a resounding array of arguments against this kind of all-encompassing genetic determinism. On the scientific side, he correctly points out that genetic influences on behavior are in most instances relatively modest. Does anyone deny that identical twins are still able to practice individual free will? After dispatching some of the sweepingly deterministic conclusions of the "science" of evolutionary psychology with a particularly effective set of rebuttals, (...) Peters arrives at the conclusion that the nature of humanness is an interaction of three things- not one, not two, but three: genetics, environment, and free will. Neglecting any one of these three leads down a path of fuzzy thinking and dangerous consequences. (shrink)

It is unknown to what extent medical researchers generalize study findings beyond their samples when their sample size, sample diversity, or knowledge of conditions that support external validity do not warrant it. It is also unknown to what extent medical researchers describe their results with precise quantifications or unquantified generalizations, i.e., generics, that can obscure variations between individuals. We therefore systematically reviewed all prospective studies (n = 533) published in the top four highest ranking medical journals, Lancet, New England Journal (...) of Medicine (NEJM), Journal of the American Medical Association (JAMA), and the British Medical Journal (BMJ), from January 2022 to May 2023. We additionally reviewed all NEJM Journal Watch clinical research summaries (n = 143) published during the same time. Of all research articles reporting prospective studies, 52.5% included generalizations beyond specific national study populations, with the numbers of articles with generics varying significantly between journals (JAMA = 12%; Lancet = 77%) (p < 0.001, V = 0.48). There was no evidence that articles containing broader generalizations or generics were correlated with larger or more nationally diverse samples. Moreover, only 10.2% of articles with generalizations beyond specific national populations reported external validity strengthening factors that could potentially support such extrapolations. There was no evidence that original research articles and NEJM Journal Watch summaries intended for practitioners differed in their use of broad generalizations, including generics. Finally, from the journal with the highest citation impact, articles containing broader conclusions were correlated with more citations. Since there was no evidence that studies with generalizations beyond specific national study populations or with generics were associated with larger, more nationally diverse samples, or with reports of population similarity that may permit extensions of conclusions, our findings suggest that the generalizations in many articles were insufficiently supported. Caution against overly broad generalizations in medical research is warranted. (shrink)

Can AI developers be held epistemically responsible for the processing of their AI systems when these systems are epistemically opaque? And can explainable AI (XAI) provide public justificatory reasons for opaque AI systems’ outputs? Koskinen (2024) gives negative answers to both questions. Here, I respond to her and argue for affirmative answers. More generally, I suggest that when considering people’s uncertainty about the factors causally determining an opaque AI’s output, it might be worth keeping in mind that a degree of (...) uncertainty about conclusions is inevitable even in entirely human-based empirical science because in induction there’s always a risk of getting it wrong. Keeping this in mind may help appreciate that requiring full transparency from AI systems before epistemically trusting their outputs might be unusually (and potentially overly) demanding. (shrink)

Many philosophers hold that generics (i.e., unquantified generalizations) are pervasive in communication and that when they are about social groups, this may offend and polarize people because generics gloss over variations between individuals. Generics about social groups might be particularly common on Twitter (X). This remains unexplored, however. Using machine learning (ML) techniques, we therefore developed an automatic classifier for social generics, applied it to 1.1 million tweets about people, and analyzed the tweets. While it is often suggested that generics (...) are ubiquitous in everyday communication, we found that most tweets (78%) about people contained no generics. However, tweets with generics received more “likes” and retweets. Furthermore, while recent psychological research may lead to the prediction that tweets with generics about political groups are more common than tweets with generics about ethnic groups, we found the opposite. However, consistent with recent claims that political animosity is less constrained by social norms than animosity against gender and ethnic groups, negative tweets with generics about political groups were significantly more prevalent and retweeted than negative tweets about ethnic groups. Our study provides the first ML-based insights into the use and impact of social generics on Twitter. (shrink)

There is an ecology of bad ideas, just as there is an ecology of weeds, and it is characteristic of the system that basic error propagates itself.–Gregory Bateson, Steps Towards an Ecology of Mind...

educated man1 Some further reflections 1 The comparison with 'reform' In reflecting, in the past, on the sort of term that 'education' is I have usually ...

This paper reviews two main historical approaches to creativity: the Romanticist approach, based on the culture of the irrational, and the Enlightenment approach, based on the culture of the objective. It defends a paradigm of creativity as a sum of rich semiotic systems that form the basis of distributed knowledge and learning, reviews historical ideas of the university, and identifies two conflicting mainstream models in regards to understanding of the university as a public good: the ‘Public’ University circa 1960–1980, and (...) the ‘post-historical’ university. Based on practical experiences, and on previous works by Peters and Jandrić, it develops the new model of ‘the creative university as digital public university’, and argues that it provides a useful philosophical goal for directing present and future practices of the contemporary university. (shrink)

PREFACE The first three of these lectures, or rather an abbreviated version of them, were first given as the Lindsay Memorial Lectures at the University of ...

This book offers a roadmap for determining when and how to regulate risky reproductive technologies on behalf of future children. It starts by explaining our intuitive, but paradoxical, belief that reproductive choices can be both life-giving and harmful. Next, it recommends a case-by-case method for reconciling the interests of future children with the reproductive liberty of prospective parents. Finally, it applies this framework to four past and future medical interventions, including cloning and genetic engineering. Drawing lessons from these case studies, (...) it criticizes the current lack of regulatory oversight. (shrink)

Many artificial intelligence (AI) systems currently used for decision-making are opaque, i.e., the internal factors that determine their decisions are not fully known to people due to the systems’ computational complexity. In response to this problem, several researchers have argued that human decision-making is equally opaque and since simplifying, reason-giving explanations (rather than exhaustive causal accounts) of a decision are typically viewed as sufficient in the human case, the same should hold for algorithmic decision-making. Here, I contend that this argument (...) overlooks that human decision-making is sometimes significantly more transparent and trustworthy than algorithmic decision-making. This is because when people explain their decisions by giving reasons for them, this frequently prompts those giving the reasons to govern or regulate themselves so as to think and act in ways that confirm their reason reports. AI explanation systems lack this self-regulative feature. Overlooking it when comparing algorithmic and human decision-making can result in underestimations of the transparency of human decision-making and in the development of explainable AI that may mislead people by activating generally warranted beliefs about the regulative dimension of reason-giving. (shrink)

Karttunen's seminal 1973 article Presuppositions of compound sentences, lays the groundwork for the elegant and fruitful theory of this subject which he subsequently presented in (1974). In (1973, pp. 185–8), however, he fallaciously argued that the regularities he discovered concerning the behavior of and, or, and if ... then in English cannot be embodied in any three-valued logic giving a truth-functional interpretation to these connectives. The present paper refutes Karttunen's argument by exhibiting an interpretation with the desired properties, and shows (...) further how the full articulation of his 1974 system can be developed naturally within a truth-conditional semantics for English if bivalence is abandoned — contrary to what Karttunen expected. (shrink)

This paper, based on an invited Thesis Eleven presentation, provides a ‘map of technopolitics’ that springs from an investigation of the theoretical notion of technological convergence adopted by the US National Science Foundation, signaling a new paradigm of ‘nano-bio-info-cogno’ technologies. This integration at the nano-level is expected to drive the next wave of scientific research, technology and knowledge economy. The paper explores the concept of ‘technopolitics’ by investigating the links between Wittgenstein’s anti-scientism and Lyotard’s ‘technoscience’, reviewing the history of the (...) notion in the work of the Belgium philosopher Gilbert Hottois. The ‘deep convergence’ representing a new technoscientific synergy is the product of long-term trends of ‘bioinformational capitalism’ that harnesses the twin forces of information and genetic sciences that coalesce in the least mature ‘cognosciences’ in their application to education and research. The map of technopolitics systematically identifies the political relations between Big Tech and ‘new digital publics’ to reveal that the new paradigm is based on the supreme value of cognitive efficiency. There are a closely-knit cluster of concerns that frame a map of political issues about the fifth-generation technological impacts on human beings, their bodies and minds, and public institutions, not least the logic of the distribution and ownership of data, information and knowledge, and its effects on democracy. (shrink)

It has been argued that our currently most satisfactory social epistemology of science can’t account for science that is based on artificial intelligence (AI) because this social epistemology requires trust between scientists that can take full responsibility for the research tools they use, and scientists can’t take full responsibility for the AI tools they use since these systems are epistemically opaque. I think this argument overlooks that much AI-based science can be done without opaque models, and that agents can take (...) full responsibility for the systems they use even if these systems are opaque. Requiring that an agent fully understand how a system works is an untenably strong condition for that agent to take full responsibility for the system and risks absolving AI developers from responsibility for their products. AI-based science need not create trust-related social epistemological problems if we keep in mind that what makes both individual scientists and their use of AI systems trustworthy isn’t full transparency of the internal processing but their adherence to social and institutional norms that ensure that scientific claims can be trusted. (shrink)

Volume 51, Issue 8, July 2019, Page 751-756.

One way of looking at conspiracy is to consider it a deliberately enhanced political weapon cultivated by those who push ‘fake news’ in a post-truth media environment. Thus, the story that Obama’s...

This book is particularly appropriate for graduate seminars or upper division courses in philosophy of mind, and for metaphysics or introductory philosophy ...

Volume 52, Issue 1, January 2020, Page 1-10.

Does algorithmic political bias contribute to an entrenchment and polarization of political positions? Franke argues that it may do so because the bias involves classifications of people as liberals, conservatives, etc., and individuals often conform to the ways in which they are classified. I provide a novel example of this phenomenon in human–computer interactions and introduce a social psychological mechanism that has been overlooked in this context but should be experimentally explored. Furthermore, while Franke proposes that algorithmic political classifications entrench (...) political identities, I contend that they may often produce the opposite result. They can lead people to change in ways that disconfirm the classifications. Consequently and counterintuitively, algorithmic political bias can in fact decrease political entrenchment and polarization. (shrink)

A principal goal of this book is to develop and apply the Situation Semantics framework. Jean Mark Gawron and Stanley Peters adopt a version of the theory in which meanings are built up via syntactically driven semantic composition rules. They provide a substantial treatment of English incorporating treatments of pronomial anaphora, quantification, donkey anaphora, and tense. The book focuses on the semantics of pronomial anaphora and quantification. The authors argue that the ambiguities of sentences with pronouns cannot be adequately (...) accounted for with a theory that represents anaphoric relations only syntactically; their relational framework uniformly deals with anaphoric relations as relations between utterances in context. They argue that there is no need for a syntactic representation of anaphoric relations, or for a theory that accounts for anaphoric ambiguities by resorting to two or more kinds of anaphora. Quantifier scope ambiguities are handled analogously to anaphoric ambiguities. This treatment integrates the Cooper Store mechanism with a theory of meaning that provides both a natural setting for it and a convincing account of what, semantically, is going on. Jean Mark Gawron is a researcher for Hewlett Packard Laboratories, Palo Alto. Stanley Peters is professor of linguistics and symbolic systems at Stanford University and is director of the Center for the Study of Language and Information. (shrink)

The list of causalities for wars in the twentieth and twenty-first centuries is horrendous with an estimated 187 million people dying in the period 1900 to the present day, with approximately 75 mi...

Teleosemantics explains mental representation in terms of biological function and selection history. One of the main objections to the account is the so-called ‘Swampman argument’ (Davidson 1987), which holds that there could be a creature with mental representation even though it lacks a selection history. A number of teleosemanticists reject the argument by emphasising that it depends on assuming a creature that is fi ctitious and hence irrelevant for teleosemantics because the theory is only concerned with representations in real-world organisms (...) (Millikan 1996, Neander 1996, 2006, Papineau 2001, 2006). I contend that this strategy doesn’t succeed. I off er an argument that captures the spirit of the original Swampman objection but relies only on organisms found in the actual world. Th e argument undermines the just mentioned response to the Swampman objection, and furthermore leads to a particular challenge to strong representationalist theories of consciousness that endorse teleosemantics such as, e.g., Dretske’s (1995) and Tye’s (1995, 2000) accounts. On these theories, the causal effi cacy of consciousness in actual creatures will be undermined. (shrink)

In 2019, the IEEE launched the P7000 standards projects intended to address ethical issues in the design of autonomous and intelligent systems. This move came amidst a growing public concern over the unintended consequences of artificial intelligence (AI), compounded by the lack of an anticipatory process for attending to ethical impact within professional practice. However, the difficulty in moving from principles to practice presents a significant challenge to the implementation of ethical guidelines. Herein, we describe two complementary frameworks for integrating (...) ethical analysis into engineering practice to help address this challenge. We then provide the outcomes of an ethical analysis informed by these frameworks, conducted within the specific context of internetdelivered therapy in digital mental health. We hope both the frameworks and analysis can provide tools and insights, not only for the context of digital healthcare, but for data-enabled and intelligent technology development more broadly. (shrink)