If you care about securing knowledge, what is wrong with being biased? Often it is said that we are less accurate and reliable knowers due to implicit biases. Likewise, many people think that biases reflect inaccurate claims about groups, are based on limited experience, and are insensitive to evidence. Chapter 3 investigates objections such as these with the help of two popular metaphors: bias as fog and bias as shortcut. Guiding readers through these metaphors, I argue that they (...) clarify the range of knowledge-related objections to implicit bias. They also suggest that there will be no unifying problem with bias from the perspective of knowledge. That is, they tell us that implicit biases can be wrong in different ways for different reasons. Finally, and perhaps most importantly, the metaphors reveal a deep—though perhaps not intractable—disagreement among theorists about whether implicit biases can be good in some cases when it comes to knowledge. (shrink)

_Anthropic Bias_ explores how to reason when you suspect that your evidence is biased by "observation selection effects"--that is, evidence that has been filtered by the precondition that there be some suitably positioned observer to "have" the evidence. This conundrum--sometimes alluded to as "the anthropic principle," "self-locating belief," or "indexical information"--turns out to be a surprisingly perplexing and intellectually stimulating challenge, one abounding with important implications for many areas in science and philosophy. There are the philosophical thought experiments and paradoxes: (...) the Doomsday Argument; Sleeping Beauty; the Presumptuous Philosopher; Adam & Eve; the Absent-Minded Driver; the Shooting Room. And there are the applications in contemporary science: cosmology ; evolutionary theory ; the problem of time's arrow ; quantum physics ; game-theory problems with imperfect recall ; even traffic analysis. _Anthropic Bias_ argues that the same principles are at work across all these domains. And it offers a synthesis: a mathematically explicit theory of observation selection effects that attempts to meet scientific needs while steering clear of philosophical paradox. (shrink)

This book represents the first major attempt by any author to provide an integrated account of the evidence for bias in human reasoning across a wide range of disparate psychological literatures. The topics discussed involve both deductive and inductive reasoning as well as statistical judgement and inference. In addition, the author proposes a general theoretical approach to the explanations of bias and considers the practical implications for real world decision making. The theoretical stance of the book is based (...) on a distinction between preconscious heuristic processes which determine the mental representation of 'relevant' features of the problem content, and subsequent analytic reasoning processes which generate inferences and judgements. Phenomena discussed and interpreted within this framework include feature matching biases in propositional reasoning, confirmation bias, biasing and debiasing effects of knowledge on reasoning, and biases in statistical judgement normally attributed to 'availability' and 'representativeness' heuristics. In the final chapter, the practical consequences of bias for real life decision making are considered, together with various issues concerning the problem of 'debiasing'. The major approaches discussed are those involving education and training on the one hand, and the development of intelligent software and interactive decision aids on the other. (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)

_Anthropic Bias_ explores how to reason when you suspect that your evidence is biased by "observation selection effects"--that is, evidence that has been filtered by the precondition that there be some suitably positioned observer to "have" the evidence. This conundrum--sometimes alluded to as "the anthropic principle," "self-locating belief," or "indexical information"--turns out to be a surprisingly perplexing and intellectually stimulating challenge, one abounding with important implications for many areas in science and philosophy. There are the philosophical thought experiments and paradoxes: (...) the Doomsday Argument; Sleeping Beauty; the Presumptuous Philosopher; Adam & Eve; the Absent-Minded Driver; the Shooting Room. And there are the applications in contemporary science: cosmology ; evolutionary theory ; the problem of time's arrow ; quantum physics ; game-theory problems with imperfect recall ; even traffic analysis. _Anthropic Bias_ argues that the same principles are at work across all these domains. And it offers a synthesis: a mathematically explicit theory of observation selection effects that attempts to meet scientific needs while steering clear of philosophical paradox. (shrink)

All else being equal, most of us typically prefer to have positive experiences in the future rather than the past and negative experiences in the past rather than the future. Recent empirical evidence tends not only to support the idea that people have these preferences, but further, that people tend to prefer more painful experiences in their past rather than fewer in their future (and mutatis mutandis for pleasant experiences). Are such preferences rationally permissible, or are they, as time-neutralists contend, (...) rationally impermissible? And what is it that grounds their having the normative status that they do have? We consider two sorts of arguments regarding the normative status of future-biased preferences. The first appeals to the supposed arbitrariness of these preferences, and the second appeals to their upshot. We evaluate these arguments in light of the recent empirical research on future-bias. (shrink)

chapter on perception and bias including implicit bias.

Data‐driven algorithms are widely used to make or assist decisions in sensitive domains, including healthcare, social services, education, hiring, and criminal justice. In various cases, such algorithms have preserved or even exacerbated biases against vulnerable communities, sparking a vibrant field of research focused on so‐called algorithmic biases. This research includes work on identification, diagnosis, and response to biases in algorithm‐based decision‐making. This paper aims to facilitate the application of philosophical analysis to these contested issues by providing an overview of three (...) key topics: What is algorithmic bias? Why and how can it occur? What can and should be done about it? Throughout, we highlight connections—both actual and potential—with philosophical ideas and concerns. (shrink)

Future-biased agents care not only about what experiences they have, but also when they have them. Many believe that A-theories of time justify future bias. Although presentism is an A-theory of time, some argue that it nevertheless negates the justification for future bias. Here, I claim that the alleged discrepancy between presentism and future bias is a special case of the cross-time relations problem. To resolve the discrepancy, I propose an account of future bias as a (...) preference for certain tensed truths properly relativized to the present. (shrink)

“Implicit bias” is a term of art referring to relatively unconscious and relatively automatic features of prejudiced judgment and social behavior. While psychologists in the field of “implicit social cognition” study “implicit attitudes” toward consumer products, self-esteem, food, alcohol, political values, and more, the most striking and well-known research has focused on implicit attitudes toward members of socially stigmatized groups, such as African-Americans, women, and the LGBTQ community.[1] For example, imagine Frank, who explicitly believes that women and men are (...) equally suited for careers outside the home. Despite his explicitly egalitarian belief, Frank might nevertheless implicitly associate women with the home, and this implicit association might lead him to behave in any number of biased ways, from trusting feedback from female co-workers less to hiring equally qualified men over women. Psychological research on implicit bias is relatively recent (§1), but a host of metaphysical (§2), epistemological (§3), and ethical questions (§4) about implicit bias are pressing.[2]. (shrink)

Our focus here is on whether, when influenced by implicit biases, those behavioural dispositions should be understood as being a part of that person’s character: whether they are part of the agent that can be morally evaluated.[4] We frame this issue in terms of control. If a state, process, or behaviour is not something that the agent can, in the relevant sense, control, then it is not something that counts as part of her character. A number of theorists have argued (...) that individuals do not have control, in the relevant sense, over the operation of implicit bias. We will argue that this claim is mistaken. We articulate and develop a notion of control that individuals have with respect to implicit bias, and argue that this kind of control can ground character-based evaluation of such behavioural dispositions. (shrink)

The rationality of future bias figures crucially in various metaphysical and ethical arguments (Prior 1959; Parfit 1984; Fischer 2019). Recently, however, philosophers have raised several arguments to the effect that future bias is irrational (Dougherty 2011; Suhler and Callender 2012; Greene and Sullivan 2015). Particularly, Greene and Sullivan (2015) claim that future bias is irrational because future bias leads to two kinds of irrational planning behaviors in agents who also seek to avoid regret. In this paper, (...) I join others who have objected to Greene and Sullivan’s argument (Dorsey 2016; Tarsney 2017), but for significantly different reasons that put into question an unchallenged premise in the debate. These reasons are based on the relationship between the alleged irrational planning behaviors and certain features of regret that regret shares with future bias. First, regret is dynamic, involving preferences that change over time and in inconsistent ways. Second, regret comes in degrees, meaning that we can rank our potential regrets. Because regret has these features, I show that the future-biased agents in Greene and Sullivan’s cases do not need to act in irrational ways to rationally avoid regret. (shrink)

Implicit biases involve associations outside conscious awareness that lead to a negative evaluation of a person on the basis of irrelevant characteristics such as race or gender. This review examines the evidence that healthcare professionals display implicit biases towards patients. PubMed, PsychINFO, PsychARTICLE and CINAHL were searched for peer-reviewed articles published between 1st March 2003 and 31st March 2013. Two reviewers assessed the eligibility of the identified papers based on precise content and quality criteria. The references of eligible papers were (...) examined to identify further eligible studies. Forty two articles were identified as eligible. Seventeen used an implicit measure, to test the biases of healthcare professionals. Twenty five articles employed a between-subjects design, using vignettes to examine the influence of patient characteristics on healthcare professionals’ attitudes, diagnoses, and treatment decisions. The second method was included although it does not isolate implicit attitudes because it is recognised by psychologists who specialise in implicit cognition as a way of detecting the possible presence of implicit bias. Twenty seven studies examined racial/ethnic biases; ten other biases were investigated, including gender, age and weight. Thirty five articles found evidence of implicit bias in healthcare professionals; all the studies that investigated correlations found a significant positive relationship between level of implicit bias and lower quality of care. The evidence indicates that healthcare professionals exhibit the same levels of implicit bias as the wider population. The interactions between multiple patient characteristics and between healthcare professional and patient characteristics reveal the complexity of the phenomenon of implicit bias and its influence on clinician-patient interaction. The most convincing studies from our review are those that combine the IAT and a method measuring the quality of treatment in the actual world. Correlational evidence indicates that biases are likely to influence diagnosis and treatment decisions and levels of care in some circumstances and need to be further investigated. Our review also indicates that there may sometimes be a gap between the norm of impartiality and the extent to which it is embraced by healthcare professionals for some of the tested characteristics. Our findings highlight the need for the healthcare profession to address the role of implicit biases in disparities in healthcare. More research in actual care settings and a greater homogeneity in methods employed to test implicit biases in healthcare is needed. (shrink)

When interests and preferences of researchers or their sponsors cause bias in experimental design, data interpretation or dissemination of research results, we normally think of it as an epistemic shortcoming. But as a result of the debate on science and values, the idea that all extra-scientific influences on research could be singled out and separated from pure science is now widely believed to be an illusion. I argue that nonetheless, there are cases in which research is rightfully regarded as (...) epistemologically deficient due to the influence of preferences on its outcomes. I present examples from biomedical research and offer an analysis in terms of social epistemology. (shrink)

What is the mental representation that is responsible for implicit bias? What is this representation that mediates between the trigger and the biased behavior? My claim is that this representation is neither a propositional attitude nor a mere association. Rather, it is mental imagery: perceptual processing that is not directly triggered by sensory input. I argue that this view captures the advantages of the two standard accounts without inheriting their disadvantages. Further, this view also explains why manipulating mental imagery (...) is among the most efficient ways of counteracting implicit bias. (shrink)

Inferences from clinical research results to estimates of therapeutic effectiveness suffer due to various biases. I argue that predictions of medical effectiveness are prone to failure because current medical research overlooks the impacts of a particularly detrimental set of biases: meta-biases. Meta-biases are linked to higher-level characteristics of medical research and their effects are only observed when comparing sets of studies that share certain meta-level properties. I offer a model for correcting research results based on meta-research evidence, the bias (...) dynamics model, which employs regularly updated empirical bias coefficients to attenuate estimates of therapeutic effectiveness. (shrink)

Research on bias in peer review examines scholarly communication and funding processes to assess the epistemic and social legitimacy of the mechanisms by which knowledge communities vet and self-regulate their work. Despite vocal concerns, a closer look at the empirical and methodological limitations of research on bias raises questions about the existence and extent of many hypothesized forms of bias. In addition, the notion of bias is predicated on an implicit ideal that, once articulated, raises questions (...) about the normative implications of research on bias in peer review. This review provides a brief description of the function, history, and scope of peer review; articulates and critiques the conception of bias unifying research on bias in peer review; characterizes and examines the empirical, methodological, and normative claims of bias in peer review research; and assesses possible alternatives to the status quo. We close by identifying ways to expand conceptions and studies of bias to countenance the complexity of social interactions among actors involved directly and indirectly in peer review. (shrink)

In this paper we make the case for the emergence of novel kind of bias with the use of algorithmic decision-making systems. We argue that the distinctive generative process of feature creation, characteristic of machine learning (ML), contorts feature parameters in ways that can lead to emerging feature spaces that encode novel algorithmic bias involving already marginalized groups. We term this bias _assembled bias._ Moreover, assembled biases are distinct from the much-discussed algorithmic bias, both in (...) source (training data versus feature creation) and in content (mimics of extant societal bias versus reconfigured categories). As such, this problem is distinct from issues arising from bias-encoding training feature sets or proxy features. Assembled bias is not epistemically transparent in source or content. Hence, when these ML models are used as a basis for decision-making in social contexts, algorithmic fairness concerns are compounded. (shrink)

Standpoint epistemologies are based on two central theses: the situated knowledge thesis and the thesis of epistemic privilege. The bias paradox suggests that there is a tension between these two notions, in the sense that they are self-contradictory. In this paper, I aim to defend standpoint epistemologies from this challenge. This defense is based on a distinction between subjective and objective justifications. According to the former, a subject S is subjectively justified in believing a proposition P iff S's belief (...) in P coheres with S's pre-existing beliefs. According to the latter, a subject S is objectively justified in believing a proposition P iff S's subjective justification is truth-conducive. I argue that, if the situated knowledge thesis is spelled out in terms of subjective justification, and if the thesis of epistemic privilege is spelled out in terms of objective justification, then there is no contradiction. If this line of thought is correct, then the bias paradox does not show that standpoint epistemologies are self-contradictory. (shrink)

This paper examines the role of prestige bias in shaping academic philosophy, with a focus on its demographics. I argue that prestige bias exacerbates the structural underrepresentation of minorities in philosophy. It works as a filter against (among others) philosophers of color, women philosophers, and philosophers of low socio-economic status. As a consequence of prestige bias our judgments of philosophical quality become distorted. I outline ways in which prestige bias in philosophy can be mitigated.

At the University of Sheffield during 2011 and 2012, a leading group of philosophers, psychologists, and others gathered to explore the nature and significance of implicit bias. The two volumes of Implicit Bias and Philosophy emerge from these workshops. Each volume philosophically examines core areas of psychological research on implicit bias as well as the ramifications of implicit bias for core areas of philosophy. Volume I: Metaphysics and Epistemology is comprised of two parts: “The Nature of (...) Implicit Attitudes, Implicit Bias, and Stereotype Threat,” and “Skepticism, Social Knowledge, and Rationality.” The first part contains chapters examining the relationship between implicit attitudes and “dual process” models of the mind; the role of affect in the formation and change of implicit associations; the unity (or disunity) of implicit attitudes; whether implicit biases are mental states at all; and whether performances on stereotype-relevant tasks are automatic and unconscious or intentional and strategic. The second part contains chapters examining implicit bias and skepticism; the effects of implicit bias on scientific research; the accessibility of social stereotypes in epistemic environments; the effects of implicit bias on the self-perception of members of stigmatized social groups as rational agents; the role of gender stereotypes in philosophy; and the role of heuristics in biased reasoning. (shrink)

Many philosophers appeal to intuitions to support some philosophical views. However, there is reason to be concerned about this practice as scientific evidence has documented systematic bias in philosophically relevant intuitions as a function of seemingly irrelevant features (e.g., personality). One popular defense used to insulate philosophers from these concerns holds that philosophical expertise eliminates the influence of these extraneous factors. Here, we test this assumption. We present data suggesting that verifiable philosophical expertise in the free will debate-as measured (...) by a reliable and validated test of expert knowledge-does not eliminate the influence of one important extraneous feature (i.e., the heritable personality trait extraversion) on judgments concerning freedom and moral responsibility. These results suggest that, in at least some important cases, the expertise defense fails. Implications for the practice of philosophy, experimental philosophy, and applied ethics are discussed. (shrink)

Philosophers have long noted, and empirical psychology has lately confirmed, that most people are “biased toward the future”: we prefer to have positive experiences in the future, and negative experiences in the past. At least two explanations have been offered for this bias: belief in temporal passage and the practical irrelevance of the past resulting from our inability to influence past events. We set out to test the latter explanation. In a large survey, we find that participants exhibit significantly (...) less future bias when asked to consider scenarios where they can affect their own past experiences. This supports the “practical irrelevance” explanation of future bias. It also suggests that future bias is not an inflexible preference hardwired by evolution, but results from a more general disposition to “accept the things we cannot change”. However, participants still exhibited substantial future bias in scenarios in which they could affect the past, leaving room for complementary explanations. Beyond the main finding, our results also indicate that future bias is stake-sensitive and that participants endorse the normative correctness of their future-biased preferences and choices. In combination, these results shed light on philosophical debates over the rationality of future bias, suggesting that it may be a rational response to empirical realities rather than a brute, arational disposition. (shrink)

In this paper, we distinguish between different sorts of assessments of algorithmic systems, describe our process of assessing such systems for ethical risk, and share some key challenges and lessons for future algorithm assessments and audits. Given the distinctive nature and function of a third-party audit, and the uncertain and shifting regulatory landscape, we suggest that second-party assessments are currently the primary mechanisms for analyzing the social impacts of systems that incorporate artificial intelligence. We then discuss two kinds of as-sessments: (...) an ethical risk assessment and a narrower, technical algo-rithmic bias assessment. We explain how the two assessments depend on each other, highlight the importance of situating the algorithm within its particular socio-technical context, and discuss a number of lessons and challenges for algorithm assessments and, potentially, for algorithm audits. The discussion builds on our team’s experience of advising and conducting ethical risk assessments for clients across dif-ferent industries in the last four years. Our main goal is to reflect on the key factors that are potentially ethically relevant in the use of algo-rithms, and draw lessons for the nascent algorithm assessment and audit industry, in the hope of helping all parties minimize the risk of harm from their use. (shrink)

Sally Haslanger has recently argued that philosophical focus on implicit bias is overly individualist, since social inequalities are best explained in terms of social structures rather than the actions and attitudes of individuals. I argue that questions of individual responsibility and implicit bias, properly understood, do constitute an important part of addressing structural injustice, and I propose an alternative conception of social structure according to which implicit biases are themselves best understood as a special type of structure.

This paper offers an unorthodox appraisal of empirical research bearing on the question of the low representation of women in philosophy. It contends that fashionable views in the profession concerning implicit bias and stereotype threat are weakly supported, that philosophers often fail to report the empirical work responsibly, and that the standards for evidence are set very low—so long as you take a certain viewpoint.

Moral, social, political, and other “nonepistemic” values can lead to bias in science, from prioritizing certain topics over others to the rationalization of questionable research practices. Such values might seem particularly common or powerful in the social sciences, given their subject matter. However, I argue first that the well-documented phenomenon of motivated reasoning provides a useful framework for understanding when values guide scientific inquiry (in pernicious or productive ways). Second, this analysis reveals a parity thesis: values influence the social (...) and natural sciences about equally, particularly because both are so prominently affected by desires for social credit and status, including recognition and career advancement. Ultimately, bias in natural and social science is both natural and social— that is, a part of human nature and considerably motivated by a concern for social status (and its maintenance). Whether the pervasive influence of values is inimical to the sciences is a separate question. (shrink)

Having a confirmation bias sometimes leads us to hold inaccurate beliefs. So, the puzzle goes: why do we have it? According to the influential argumentative theory of reasoning, confirmation bias emerges because the primary function of reason is not to form accurate beliefs, but to convince others that we’re right. A crucial prediction of the theory, then, is that confirmation bias should be found only in the reasoning domain. In this article, we argue that there is evidence (...) that confirmation bias does exist outside the reasoning domain. This undermines the main evidential basis for the argumentative theory of reasoning. In presenting the relevant evidence, we explore why having such confirmation bias may not be maladaptive. (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)

Are individuals morally responsible for their implicit biases? One reason to think not is that implicit biases are often advertised as unconscious, ‘introspectively inaccessible’ attitudes. However, recent empirical evidence consistently suggests that individuals are aware of their implicit biases, although often in partial and inarticulate ways. Here I explore the implications of this evidence of partial awareness for individuals’ moral responsibility. First, I argue that responsibility comes in degrees. Second, I argue that individuals’ partial awareness of their implicit biases makes (...) them (partially) morally responsible for them. I argue by analogy to a close relative of implicit bias: moods. (shrink)

Nearly everyone prefers pain to be in the past rather than the future. This seems like a rationally permissible preference. But I argue that appearances are misleading, and that future-biased preferences are in fact irrational. My argument appeals to trade-offs between hedonic experiences and other goods. I argue that we are rationally required to adopt an exchange rate between a hedonic experience and another type of good that stays fixed, regardless of whether the hedonic experience is in the past or (...) future. (shrink)

Recently, amid growing awareness that computer algorithms are not neutral tools but can cause harm by reproducing and amplifying bias, attempts to detect and prevent such biases have intensified. An approach that has received considerable attention in this regard is the Value Sensitive Design (VSD) methodology, which aims to contribute to both the critical analysis of (dis)values in existing technologies and the construction of novel technologies that account for specific desired values. This article provides a brief overview of the (...) key features of the Value Sensitive Design approach, examines its contributions to understanding and addressing issues around bias in computer systems, outlines the current debates on algorithmic bias and fairness in machine learning, and discusses how such debates could profit from VSD-derived insights and recommendations. Relating these debates on values in design and algorithmic bias to research on cognitive biases, we conclude by stressing our collective duty to not only detect and counter biases in software systems, but to also address and remedy their societal origins. (shrink)

This book is a philosophical exploration of bias and our practices of attributing it. It develops and defends the norm-theoretic account of bias, according to which objectionable biases involve systematic departures from objective norms or standards of correctness. It explores the perspectival character of bias attributions, or the ways in which our views about which people and sources of information are biased about a topic are influenced and constrained, both rationally and psychologically, by our views about the (...) topic itself. The book defends a robust pluralism about bias, according to which a radical diversity of things are genuinely biased, with none of these more fundamental than all of the rest. Biases of people are understood as multiply realizable dispositions to depart from objective norms. It offers a novel account of the bias blind spot, or our tendency to fail to see bias in ourselves in a way that we see it in others. It explores the connections between bias and central topics in the theory of knowledge, including truth, knowledge, rationality, reliability, introspection, skepticism, and disagreement. A number of racial conclusions are defended: that both rationality and morality sometimes require us to be biased; that in many cases of disagreement, we are rationally required to view those who disagree with us as biased, even if we know nothing about how they arrived at their views or why they hold them; and that even God could not have made us reliable detectors of our own biases through introspection. (shrink)

Following Kahneman and Tversky, I examine the term ‘bias’ as it is used to refer to systematic errors. Given the central role of error in this understanding of bias, it is helpful to consider what it is to err and to distinguish different kinds of error. I identify two main kinds of error, examine ethical issues that pertain to the relation of these types of error, and explain their moral significance. Next, I provide a four-level explanatory framework for (...) understanding biases: personal, sub-personal, situational, and systemic levels. Finally, I examine some of the ethical complexities involved in attributing biases to oneself and to others. (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)

To analyze which ethically relevant biases have been identified by academic literature in artificial intelligence algorithms developed either for patient risk prediction and triage, or for contact tracing to deal with the COVID-19 pandemic. Additionally, to specifically investigate whether the role of social determinants of health have been considered in these AI developments or not. We conducted a scoping review of the literature, which covered publications from March 2020 to April 2021. ​Studies mentioning biases on AI algorithms developed for contact (...) tracing and medical triage or risk prediction regarding COVID-19 were included. From 1054 identified articles, 20 studies were finally included. We propose a typology of biases identified in the literature based on bias, limitations and other ethical issues in both areas of analysis. Results on health disparities and SDOH were classified into five categories: racial disparities, biased data, socio-economic disparities, unequal accessibility and workforce, and information communication. SDOH needs to be considered in the clinical context, where they still seem underestimated. Epidemiological conditions depend on geographic location, so the use of local data in studies to develop international solutions may increase some biases. Gender bias was not specifically addressed in the articles included. The main biases are related to data collection and management. Ethical problems related to privacy, consent, and lack of regulation have been identified in contact tracing while some bias-related health inequalities have been highlighted. There is a need for further research focusing on SDOH and these specific AI apps. (shrink)

(This contribution is primarily based on "Implicit Bias, Moods, and Moral Responsibility," (2018) Pacific Philosophical Quarterly. This version has been shortened and significantly revised to be more accessible and student-oriented.) Are individuals morally responsible for their implicit biases? One reason to think not is that implicit biases are often advertised as unconscious. However, recent empirical evidence consistently suggests that individuals are aware of their implicit biases, although often in partial and inarticulate ways. Here I explore the implications of this (...) evidence of partial awareness for individuals’ moral responsibility. First, I argue that responsibility comes in degrees. Second, I argue that individuals’ partial awareness of their implicit biases makes them (partially) morally responsible for them. I argue by analogy to a close relative of implicit bias: moods. (shrink)

Online information intermediaries such as Facebook and Google are slowly replacing traditional media channels thereby partly becoming the gatekeepers of our society. To deal with the growing amount of information on the social web and the burden it brings on the average user, these gatekeepers recently started to introduce personalization features, algorithms that filter information per individual. In this paper we show that these online services that filter information are not merely algorithms. Humans not only affect the design of the (...) algorithms, but they also can manually influence the filtering process even when the algorithm is operational. We further analyze filtering processes in detail, show how personalization connects to other filtering techniques, and show that both human and technical biases are present in today’s emergent gatekeepers. We use the existing literature on gatekeeping and search engine bias and provide a model of algorithmic gatekeeping. (shrink)