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)

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)

Human freedom and the autonomy of art : Adorno as a reader of Kant -- The ephemeral and the absolute : provisional notes to Adorno's aesthetic theory -- Aesthetic violence : the concept of the ugly in Adorno's aesthetic theory -- Reality, realism, and representation -- A precarious balance : Adorno and German classicism.

‘No-platforming’—the practice of denying someone the opportunity to express their opinion at certain venues because of the perceived abhorrent or misguided nature of their view—is a hot topic. Several philosophers have advanced epistemic reasons for using the policy in certain cases. Here we introduce epistemic considerations against no-platforming that are relevant for the reflection on the cases at issue. We then contend that three recent epistemic arguments in favor of no-platforming fail to factor these considerations in and, as a result, (...) offer neither a conclusive justification nor strong epistemic support for no-platforming in any of the relevant cases. Moreover, we argue that, taken together, our epistemic considerations against no-platforming and the three arguments for the policy suggest that no-platforming poses an epistemic dilemma. While advocates and opponents of no-platforming alike have so far overlooked this dilemma, it should be addressed not only to prevent that actual no-platforming decisions create more epistemic harm than good, but also to put us into a better position to justify the policy when it is indeed warranted. (shrink)

Confirmation bias is one of the most widely discussed epistemically problematic cognitions, challenging reliable belief formation and the correction of inaccurate views. Given its problematic nature, it remains unclear why the bias evolved and is still with us today. To offer an explanation, several philosophers and scientists have argued that the bias is in fact adaptive. I critically discuss three recent proposals of this kind before developing a novel alternative, what I call the ‘reality-matching account’. According to the account, confirmation (...) bias evolved because it helps us influence people and social structures so that they come to match our beliefs about them. This can result in significant developmental and epistemic benefits for us and other people, ensuring that over time we don’t become epistemically disconnected from social reality but can navigate it more easily. While that might not be the only evolved function of confirmation bias, it is an important one that has so far been neglected in the theorizing on the bias. (shrink)

This concluding chapter proposes an alternative interpretation of Theodor W. Adorno's unfinished Aesthetic Theory in which he reconsiders some of the philosopher's central aesthetic concepts, such as aesthetic autonomy, from the perspective of those moments when Adorno's writing appears to destabilize the work of art and, by extension, the philosophical claims that his theories generally are held to make on behalf of the aesthetic. Adorno's Aesthetic Theory, initially shunned or attacked when it was posthumously published in 1970, has become increasingly (...) his most widely and carefully read work. While the interpreters are still in disagreement about the appropriate reading of the text, there is largely consensus about its significance as the culmination of Adorno's œuvre and its importance for the contemporary debate on aesthetics. (shrink)

When scientists or science reporters communicate research results to the public, this often involves ethical and epistemic risks. One such risk arises when scientific claims cause cognitive or behavioural changes in the audience that contribute to the self-fulfilment of these claims. I argue that the ethical and epistemic problems that such self-fulfilment effects may pose are much broader and more common than hitherto appreciated. Moreover, these problems are often due to a specific psychological phenomenon that has been neglected in the (...) research on science communication: many people tend to conform to ‘descriptive norms’, that is, norms capturing (perceptions of) what others commonly do, think, or feel. Because of this tendency, science communication may frequently produce significant social harm. I contend that scientists have a responsibility to assess the risk of this potential harm and consider adopting strategies to mitigate it. I introduce one such strategy and argue that its implementation is independently well motivated by the fact that it helps improve scientific accuracy. (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)

Members of the field of philosophy have, just as other people, political convictions or, as psychologists call them, ideologies. How are different ideologies distributed and perceived in the field? Using the familiar distinction between the political left and right, we surveyed an international sample of 794 subjects in philosophy. We found that survey participants clearly leaned left (75%), while right-leaning individuals (14%) and moderates (11%) were underrepresented. Moreover, and strikingly, across the political spectrum, from very left-leaning individuals and moderates to (...) very right-leaning individuals, participants reported experiencing ideological hostility in the field, occasionally even from those from their own side of the political spectrum. Finally, while about half of the subjects believed that discrimination against left- or right-leaning individuals in the field is not justified, a significant minority displayed an explicit willingness to discriminate against colleagues with the opposite ideology. Our findings are both surprising and important, because a commitment to tolerance and equality is widespread in philosophy, and there is reason to think that ideological similarity, hostility, and discrimination undermine reliable belief formation in many areas of the discipline. (shrink)

Some artificial intelligence systems can display algorithmic bias, i.e. they may produce outputs that unfairly discriminate against people based on their social identity. Much research on this topic focuses on algorithmic bias that disadvantages people based on their gender or racial identity. The related ethical problems are significant and well known. Algorithmic bias against other aspects of people’s social identity, for instance, their political orientation, remains largely unexplored. This paper argues that algorithmic bias against people’s political orientation can arise in (...) some of the same ways in which algorithmic gender and racial biases emerge. However, it differs importantly from them because there are strong social norms against gender and racial biases. This does not hold to the same extent for political biases. Political biases can thus more powerfully influence people, which increases the chances that these biases become embedded in algorithms and makes algorithmic political biases harder to detect and eradicate than gender and racial biases even though they all can produce similar harm. Since some algorithms can now also easily identify people’s political orientations against their will, these problems are exacerbated. Algorithmic political bias thus raises substantial and distinctive risks that the AI community should be aware of and examine. (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)

In the philosophy of science, it is a common proposal that values are illegitimate in science and should be counteracted whenever they drive inquiry to the confirmation of predetermined conclusions. Drawing on recent cognitive scientific research on human reasoning and confirmation bias, I argue that this view should be rejected. Advocates of it have overlooked that values that drive inquiry to the confirmation of predetermined conclusions can contribute to the reliability of scientific inquiry at the group level even when they (...) negatively affect an individual’s cognition. This casts doubt on the proposal that such values should always be illegitimate in science. It also suggests that advocates of that proposal assume a narrow, individualistic account of science that threatens to undermine their own project of ensuring reliable belief formation in science. (shrink)

It has been argued that implicit biases are operative in philosophy and lead to significant epistemic costs in the field. Philosophers working on this issue have focussed mainly on implicit gender and race biases. They have overlooked ideological bias, which targets political orientations. Psychologists have found ideological bias in their field and have argued that it has negative epistemic effects on scientific research. I relate this debate to the field of philosophy and argue that if, as some studies suggest, the (...) same bias also exists in philosophy then it will lead to hitherto unrecognised epistemic hazards in the field. Furthermore, the bias is epistemically different from the more familiar biases in respects that are important for epistemology, ethics, and metaphilosophy. (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)

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)

This article holds that ability to support one’s opinions with arguments, awareness of the arguments for other opinions, and insight into the superiority of some arguments are basic requirements for rational discourse. Based on a content analysis of Swiss Italian newspaper coverage of a controversy over a smoke-free law introduced and finally implemented in the canton of Ticino in 2007 and on a five-wave panel survey of public opinion on the issue, the article describes elements of the argumentative structure of (...) news coverage of this issue and relates them to rationality requirements. It finds that the newspapers offered a wide variety of arguments to readers (articulation function) when political activity to introduce a ban intensified, and especially when arguments mattered as a referendum approached. Papers displayed a reasonable and differentiated picture of arguments, contributing to a unique pattern of weighting and evaluating them. Neither newspaper coverage nor public opinion showed much sign of change, which speaks against a reaction to superior arguments. Finally, the newspaper bias towards arguments for the ban and their inclination to publish objections to arguments against may have contributed to a widespread lack of awareness of arguments against the ban among people favouring it. Thus, evidence of the rationality of argumentative structures is mixed, and further enquiry into this subject is encouraged. (shrink)

Scientists may sometimes generalize from their samples to broader populations when they have not yet sufficiently supported this generalization. Do such hasty generalizations also occur in experimental philosophy? To check, we analyzed 171 experimental philosophy studies published between 2017 and 2023. We found that most studies tested only Western populations but generalized beyond them without justification. There was also no evidence that studies with broader conclusions had larger, more diverse samples, but they nonetheless had higher citation impact. Our analyses reveal (...) important methodological limitations of many experimental philosophy studies and suggest that philosophical training may not protect against hasty generalizations. (shrink)

Many ethical frameworks require artificial intelligence (AI) systems to be explainable. Explainable AI (XAI) models are frequently tested for their adequacy in user studies. Since different people may have different explanatory needs, it is important that participant samples in user studies are large enough to represent the target population to enable generalizations. However, it is unclear to what extent XAI researchers reflect on and justify their sample sizes or avoid broad generalizations across people. We analyzed XAI user studies (N = (...) 220) published between 2012 and 2022. Most studies did not offer rationales for their sample sizes. Moreover, most papers generalized their conclusions beyond their target population, and there was no evidence that broader conclusions in quantitative studies were correlated with larger samples. These methodological problems can impede evaluations of whether XAI systems implement the explainability called for in ethical frameworks. We outline principles for more inclusive XAI user studies. (shrink)

By drawing on empirical evidence, Matt King and Peter Carruthers have recently argued that there are no conscious propositional attitudes, such as decisions, and that this undermines moral responsibility. Neil Levy responds to King and Carruthers, and claims that their considerations needn’t worry theorists of moral responsibility. I argue that Levy’s response to King and Carruthers’ challenge to moral responsibility is unsatisfactory. After that, I propose what I take to be a preferable way of dealing with their challenge. I offer (...) an account of moral responsibility that ties responsibility to consciously deciding to do X, as opposed to a conscious decision to do X. On this account, even if there are no conscious decisions, moral responsibility won’t be undermined. (shrink)

Why do we engage in folk psychology, that is, why do we think about and ascribe propositional attitudes such as beliefs, desires, intentions etc. to people? On the standard view, folk psychology is primarily for mindreading, for detecting mental states and explaining and/or predicting people’s behaviour in terms of them. In contrast, McGeer (1996, 2007, 2015), and Zawidzki (2008, 2013) maintain that folk psychology is not primarily for mindreading but for mindshaping, that is, for moulding people’s behavior and minds (e.g., (...) via the imposition of social norms) so that coordination becomes easier. Mindreading is derived from and only as effective as it is because of mindshaping, not vice versa. I critically assess McGeer’s, and Zawidzki’s proposal and contend that three common motivations for the mindshaping view do not provide sufficient support for their particular version of it. I argue furthermore that their proposal underestimates the role that epistemic processing plays for mindshaping. And I provide reasons for favouring an alternative according to which, in social cognition involving ascriptions of propositional attitudes, neither mindshaping nor mindreading is primary but both are complementary in that effective mindshaping depends as much on mindreading as effective mindreading depends on mindshaping. (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)

To better navigate social interactions, we routinely (consciously or unconsciously) categorize people based on their distinctive features. One important way we do this is by ascribing political orientations to them. For example, based on certain behavioral cues, we might perceive someone as politically liberal, progressive, conservative, libertarian, Marxist, anarchist, or fascist. Although such ascriptions may appear to be mere descriptions, I argue that they can have deeper, regulative effects on their targets, potentially politicizing and polarizing them in ways that remain (...) underexplored in research on political polarization. To capture this dynamic, I introduce the notion of ‘politicizing mindshaping,’ distinguish it from other types of politicization, and review evidence suggesting that politicizing mindshaping is likely common. Finally, I contend that the pernicious effects of politicizing mindshaping may currently be significantly underestimated. This is because, unlike in many other cases of social labeling, negativity in political labeling is, to some extent, widely tolerated – even encouraged – and largely unconstrained by social norms in democratic countries. (shrink)

Advances in genetic research have created the need to inform consumers. Yet, the communication of hereditary risk and of the options for how to deal with it is a difficult task. Due to the abstract nature of genetics, people tend to overestimate or underestimate their risk. This paper addresses the issue of how to communicate risk information on hereditary breast and ovarian cancer through an online application. The core of the paper illustrates the design of OPERA, a risk assessment instrument (...) that applies the UK National Institute of Health and Clinical Excellence's guidelines on the basis of (i) the number of relatives on the same side of the family with the same cancer or cancers that are known to run together; (ii) the ages of these relatives at diagnosis and (iii) the closeness of the family relationship with the person who is doing the assessment. By relying on the argumentation theory, we explain how the communication strategy that OPERA implements is essentially based on Perelman and Olbrechts-Tyteca's deductive argumentation by association. By using as premises “facts” (propositions about reality that can be assumed without further justification) and “truths” (propositions that make connections about facts), OPERA delivers its claims with an ex auctoritate causal link aimed at transferring the audience's acceptance of the cause to the effect. Overall, the design of OPERA rests on its capacity to induce users' active processing of risk information through an appeal to their reasoning faculty. In the conclusion, we present some results from a pilot evaluation of users' acceptance of OPERA. (shrink)

Marcus Lutter is one of the most renowned legal scholars in Germany and has significantly influenced commercial law over the past few decades, particularly the law of limited liability companies and the law of stock corporations. Issued in honor of his 80th birthday, this collected volume presents the 65 most important essays by Marcus Lutter on matters of company law. It particularly addresses the bodies and members of the bodies of a company, the affiliated group, capital protection, transformation law and (...) financial accounting, as well as the principle national and international issues concerning business and company law and legal policy. (shrink)

It has recently been suggested that politically motivated cognition leads progressive individuals to form beliefs that underestimate real differences between social groups and to process information selectively to support these beliefs and an egalitarian outlook. I contend that this tendency, which I shall call ‘egalitarian confirmation bias’, is often ‘Mandevillian’ in nature. That is, while it is epistemically problematic in one’s own cognition, it often has effects that significantly improve other people’s truth tracking, especially that of stigmatized individuals in academia. (...) Due to its Mandevillian character, egalitarian confirmation bias isn’t only epistemically but also ethically beneficial, as it helps decrease social injustice. Moreover, since egalitarian confirmation bias has Mandevillian effects especially in academia, and since progressives are particularly likely to display the bias, there is an epistemic reason for maintaining the often-noted political majority of progressives in academia. That is, while many researchers hold that diversity in academia is epistemically beneficial because it helps reduce bias, I argue that precisely because political diversity would help reduce egalitarian confirmation bias, it would in fact in one important sense be epistemically costly. (shrink)

It is well known that on the Internet, computer algorithms track our website browsing, clicks, and search history to infer our preferences, interests, and goals. The nature of this algorithmic tracking remains unclear, however. Does it involve what many cognitive scientists and philosophers call ‘mindreading’, i.e., an epistemic capacity to attribute mental states to people to predict, explain, or influence their actions? Here I argue that it does. This is because humans are in a particular way embedded in the process (...) of algorithmic tracking. Specifically, if we endorse common conditions for extended cognition, then human mindreading (by website operators and users) is often literally extended into, that is, partly realized by, not merely causally coupled to, computer systems performing algorithmic tracking. The view that human mindreading extends outside the body into computers in this way has significant ethical advantages. It points to new conceptual ways to reclaim our autonomy and privacy in the face of increasing risks of computational control and online manipulation. These benefits speak in favor of endorsing the notion of extended mindreading. (shrink)

Drawing on empirical findings, a number of philosophers have recently argued that people who use English as a foreign language may face a linguistic bias in academia in that they or their contributions may be perceived more negatively than warranted because of their English. I take a critical look at this argument. I first distinguish different phenomena that may be conceptualized as linguistic bias but that should be kept separate to avoid overgeneralizations. I then examine a range of empirical studies (...) that philosophers have cited to argue that people who use English as a foreign language are subject to linguistic bias in academia. I contend that many of these studies do not sufficiently support key claims that philosophers have made about linguistic bias, are challenged by counterevidence, and lack generalizability. I end by introducing methodological recommendations that may help philosophers develop more convincing empirically informed arguments regarding linguistic bias. (shrink)