AI systems that demonstrate significant bias or lower than claimed accuracy, and resulting in individual and societal harms, continue to be reported. Such reports beg the question as to why such systems continue to be funded, developed and deployed despite the many published ethical AI principles. This paper focusses on the funding processes for AI research grants which we have identified as a gap in the current range of ethical AI solutions such as AI procurement guidelines, AI impact (...) assessments and AI audit frameworks. We highlight the responsibilities of funding bodies to ensure investment is channelled towards trustworthy and safe AI systems and provides case studies as to how other ethical funding principles are managed. We offer a first sight of two proposals for funding bodies to consider regarding procedures they can employ. The first proposal is for the inclusion of a Trustworthy AI Statement’ section in the grant application form and offers an example of the associated guidance. The second proposal outlines the wider management requirements of a funding body for the ethical review and monitoring of funded projects to ensure adherence to the proposed ethical strategies in the applicants Trustworthy AI Statement. The anticipated outcome for such proposals being employed would be to create a ‘stop and think’ section during the project planning and application procedure requiring applicants to implement the methods for the ethically aligned design of AI. In essence it asks funders to send the message “if you want the money, then build trustworthy AI!”. (shrink)

Peer reviewers at many funding agencies and scientific journals are asked to score submissions both on individual criteria and overall. The overall scores should be some kind of aggregate of the criteria scores. Carole Lee identifies this as a potential locus for bias to enter the peer review process, which she calls commensuration bias. Here I view the aggregation of scores through the lens of social choice theory. I argue that, when reviewing grant proposals, it is in (...) many cases impossible to avoid commensuration bias. (shrink)

The growing commercialization of scientific research has raised important concerns about industry bias. According to some evidence, so-called industry bias can affect the integrity of the science as well as the direction of the research agenda. I argue that conceptualizing industry’s influence in scientific research in terms of bias is unhelpful. Insofar as industry sponsorship negatively affects the integrity of the research, it does so through biasing mechanisms that can affect any research independently of the source of (...) funding. Talk about industry bias thus offers no insight into the particular epistemic shortcomings at stake. If the concern is with the negative effects that industry funding can have on the research agenda, conceptualizing this influence as bias obscures the ways in which such impact is problematic and limits our ability to offer solutions that can successfully address the concerns raised by the growing role of private funding in science. (shrink)

This article develops a constructive criticism of methodological conventionalism. Methodological conventionalism asserts that standards of inductive risk ought to be justified in virtue of their ability to facilitate coordination in a research community. On that view, industry bias occurs when conventional methodological standards are violated to foster industry preferences. The underlying account of scientific conventionality, however, is problematically incomplete. Conventions may be justified in virtue of their coordinative functions, but often qualify for posterior empirical criticism as research advances. Accordingly, (...) industry bias does not only threaten existing conventions but may impede their empirically warranted improvement if they align with industry preferences. My empiricist account of standards of inductive risk avoids such a problem by asserting that conventional justification can be pragmatically warranted but has, in principle, only a provisional status. Methodological conventions, therefore, should not only be defended from preference-based infringements on their coordinative function but ought to be subjected to empirical criticism. (shrink)

High percentages of submitted papers are rejected at editorial levels without offering a second chance to authors by sending their papers for further peer-reviews. In most cases, the rejections are typical quick answers without helpful argumentations related to the content of the rejected material. More surprisingly, some journals vaunt their high rejection rates as a “mark of prestige”!However, journals that reject high percentages of submitted papers have built their prominent positions based on a flawed measure, the impact factor, and from (...) a long and favorable historical context. Their shareholders may think that they are allowed to have a large margin of rejection rates without affecting their sponsorship or funding sources thanks to an extended anchorage since tens, or in some cases hundreds, of years compared to unknown or new journals that struggle to pave a way in the scientific publication world. Historical anchorage of some journals also makes it unfair to compare old and new .. (shrink)

The results of forensic science are believed to be reliable, and are widely used in support of verdicts around the world. However, due to the lack of suitable empirical studies, we actually know very little about the reliability of such results. In this paper, I argue that phenomena analogous to the main culprits for the replication crisis in psychology are also present in forensic science. Therefore forensic results are significantly less reliable than is commonly believed. I conclude that in order (...) to obtain reliable estimates for the reliability of forensic results, we need to conduct studies analogous to the large-scale replication projects in psychology. Additionally, I point to some ways for improving the reliability of forensic science, inspired by the reforms proposed in response to the replicability crisis. (shrink)

With twentieth- and twenty-first-century philosophy of science’s unfolding acceptance of the nature of scientific inquiry being value-laden, the persistent worry has been that there are no means for legitimate negotiation of the social or non-epistemic values that enter into science. The rejection of the value-free ideal in science has thereby been coupled with the spectres of indiscriminate relativism and bias in scientific inquiry. I challenge this view in the context of recently expressed concerns regarding Canada's death of evidence controversy. (...) The worry, raised by Stathis Psillos, is that as constructivist accounts of science demoted the previously secure status of evidence for drawing justified conclusions in science, we were left with no rational delineation between the right and wrong values for science. The implication for the death of evidence controversy is that we may have no rational grounds for claiming that the Canadian Government is wrong to interfere with scientific enterprise. But he does offer another avenue for reaching the conclusion that the wrong social values are directing the current stifling of some sectors of Canadian science. Psillos draws from standpoint epistemologies to devise a salient defence of ‘valuing evidence’ as a universalizable social value. That is, government bodies ought to enable scientific research via adequate funding as well as political non-interference. In this paper, I counter that non-epistemic values can be rationally evaluated and that standpoint epistemology’s universalizable standpoint provides an inadequate framework for negotiating social values in science. Regarding, I draw from the evidence-based medicine debate in philosophy of medicine and from feminist empiricist investigations into the science–values relationship in order to make the argument for empirically driven value arbitration. If social values can be rationally chosen in the context of justification, then we can have grounds for charging the Canadian leadership with being ‘at war with science’. I further argue that my recommended empiricist methodology is preferable to Psillos’s search for universalizable perspectives for negotiating social values in science because the latter method permits little more than the trivial conclusion that evidence is valuable to science. (shrink)

In its recent statement 'Sex Selection and Preimplantation Genetic Diagnosis', the Ethics Committee of the American Society of Reproductive Medicine concluded that preimplantation genetic diagnosis for sex selection for non-medical reasons should be discouraged because it poses a risk of unwarranted gender bias, social harm, and results in the diversion of medical resources from genuine medical need. We critically examine the arguments presented against sex selection using preimplantation genetic diagnosis. We argue that sex selection should be available, at least (...) within privately funded health care. (shrink)

Eyal Shahar’s essay review [1] of James Penston’s remarkable book [2] seems more inspired playful academic provocation than review or essay, expressing dramatic views of impossible validity. The account given of modern biostatistical causation reveals the slide from science into the intellectual confusion and non-science RCTs have created: “…. the purpose of medical research is to estimate the magnitude of the effect of a causal contrast, for example the probability ratio of a binary outcome …” But Shahar’s world is simultaneously (...) not probabilistic, but of absolute uncertainty: “We should have no confidence in any type of evidence ….. We should have no confidence at all”. Shahar’s "Causal contrast" is attractive. It seems to make sense, but bypasses in two words the means of establishing causation by the scientific method. This phrase assumes a numeric statistically significant “contrast” is causal rather than a potential correlation requiring further investigation. The concept of “causal contrast” is a slippery slope from sense into biostatistical non-science. This can be illustrated with an hypothetical RCT where 100% of interventions exhibit a posited treatment effect and 0% of placebo controls. Internal validity is seemingly quite reasonably assumed satisfied (common-sense dictating the likelihood of an awesome magnificent fraud, bias or plain error of the magnitude required is infinitesimal). Scientific method appears satisfied. The RCT demonstrates: (1) strict regularity of outcome in the presence of posited cause; (2) the absence of outcome in its absence and (3) an intervention (experiment) showing the direction of causation is from posited cause to posited effect. Now travel further down the slope from science. Assume 50% of interventions and 0% of controls are positive. We compromise scientific method, but justify this by assuming a large subgroup which we say surely must on these figures be exhibiting the posited treatment effect. But what of 10% of interventions and 9% of placebo controls exhibiting the posited treatment effect? Our biostatistician says the 1% “causal contrast” is statistically significant. But we have: (1) minimal evidence of regularity; (2) the posited outcome irrespective of presence of posited cause and (3) our intervention is at the highest equivocal in demonstrating any form of causation. This is not science. It is, however, where biostatistics has unthinkingly taken us, as Penston has shown comprehensively [2]. We, the audience of published medical research, are now for the 10% / 9% example well down the slope from science. An unattractive hypothesis results requiring numerous assumptions similar to these:- "There is a 'contrast' which is ‘causal’, albeit the method employed is not scientific. An effect of the intervention has been observed in a very small subgroup. This subgroup is susceptible to treatment. The similar number of placebo controls exhibiting the outcome sought is irrelevant, because the 1% difference between intervention and controls is statistically significant. The statistical analysis is valid and reliable. The RCT’s internal validity is sufficiently satisfied. No funding or bias or fraud has affected the results or their analysis.” As Penston notes: “Confirming and refuting the results of research is crucial to science …. But … there’s no way of testing the results of any particular large-scale RCT or epidemiological study. Each study … is left hanging in the air, unsupported.” It gets worse. To identify a rare serious adverse reaction of a frequency of 1:10,000 can require a trial of 200,000 or larger split between controls and interventions. This is not done. But for every 100 who prospectively benefit from the intervention, 9,900 also receive it. And for every 100 benefiting one person (who likely gains no benefit) will suffer a serious unidentified adverse reaction. This is also without taking account of more common adverse reactions whether serious or otherwise. References [1] Shahar, E. (2011) Research and medicine: human conjectures at every turn. International Journal of Person Centered Medicine 1 (2), 250-253. [2] Penston, J. (2010). stats.con: How we’ve been fooled by statistics-based research in medicine. London: The London Press, UK. (shrink)

Introduction Current treatments for pain have limited benefits and worrying side effects. Some studies suggest that pain is reduced when clinicians deliver positive messages. However, the effects of positive messages are heterogeneous and have not been subject to meta-analysis. We aimed to estimate the efficacy of positive messages for pain reduction. -/- Methods We included randomized trials of the effects of positive messages in a subset of the studies included in a recent systematic review of context factors for treating pain. (...) Several electronic databases were searched. Reference lists of relevant studies were also searched. Two authors independently undertook study selection, data extraction, risk of bias assessment, and analyses. Our primary outcome measures were differences in patient- or observer-reported pain between groups who were given positive messages and those who were not. -/- Results Of the 16 randomized trials (1703 patients) that met the inclusion criteria, 12 trials had sufficient data for meta-analysis. The pooled standardized effect size was −0.31 (95% confidence interval [CI] −0.61 to −0.01, p = 0.04, I2 = 82%). The effect size remained positive but not statistically significant after we excluded studies considered to have a high risk of bias (standard effect size −0.17, 95% CI −0.54 to 0.19, P = 0.36, I2 = 84%). -/- Conclusion Care of patients with chronic or acute pain may be enhanced when clinicians deliver positive messages about possible clinical outcomes. However, we have identified several limitations of the present study that suggest caution when interpreting the results. We recommend further high-quality studies to confirm (or falsify) our result. -/- FUNDING -/- Alexander Mebius research has been funded through the ERC grant "Philosophy of Pharmacology: Safety, Statistical Standards, and Evidence Amalgamation" (GA: 639276). (shrink)

Mainstream views of medical research tell us it should be a fact-based, value-free endeavor: what a scientist (or her funding source) wants or cares about should not influence her findings. At the same time, we also sometimes criticize medical research for failing to embody certain values, e.g. when we criticize pharmaceutical companies for largely ignoring the diseases that affect the global poor. This chapter seeks to reconcile these perspectives by distinguishing appropriate from inappropriate influences of values on medical research. (...) It divides this broad question into two narrower ones, the Role Question (at what points in the research process is value influence potentially acceptable?) and the Content Question (when value influence is potentially acceptable, what specific values should researchers use?), and then draws on the philosophical literature on values in science to explore answers to each of them. (shrink)

Journal impact factor is a value calculated annually based on the number of times articles published in a journal are cited in two, or more, of the preceding years. At the time of its inception in 1955 , the inventor of the impact factor did not imagine that 1 day his tool would become a controversial and abusive measure, as he confessed 44 years later . The impact factor became a major detrimental factor of quality, creating huge pressures on authors, (...) editors, stakeholders and funders. More tragically, in some countries the number of publications in journals with “high impact factors” condition the allocation of government funding for entire institutions . Based on the assumption that IF reflects scientific quality, the impact factor produces a widespread impression of prestige and reputation, though no experimental data support this hypothesis .The impact factor was originally conceived as a bibliome .. (shrink)

Dubljević and company’s proposed approach for incorporating a socio-political perspective into neuroethics has clear potential to help mitigate the effects of research ‘hype’ relating to neuroethics. Their approach serves as a social regulation meant to improve the realizability of neuroethics research. Drawing on Dubljević et al. s suggestion, we consider how incorporating a socio-political perspective in other scientific disciplines could help the scientific community as a whole move beyond the infamous ‘reproducibility crisis’ in science. The reproducibility crisis is a concern (...) in science stemming from the revelation that most scientific results are difficult, or impossible, to replicate (Frias-Navarro et al., 2020). By applying the four principles proposed by Dubljević et al., the scientific community can avoid many of the pitfalls that have led to the reproducibility crisis, such as the file drawer effect, a publication bias preventing negative results from being published, or the publish-or-perish culture - the pressure for scientists to publish highly influential papers in high impact journals in order to keep their funding (Rosenthal, 1979; Begley & Ioannidis, 2015). As such, we argue that, in its final form, this socio-political framework begs for true open science, wherein systematically sharing data would enable enlightened policymaking by showing a picture closer to the ground-level realities of scientific research. (shrink)

Variations in people’s perceptions of investment risk and financial literacy have been linked in studies. More specifically, Diacon (2016) discovered significant differences between less financially savvy non-experts and financial professionals. Lay people therefore have a larger propensity for association bias (i.e., they give suppliers and salesmen a higher level of credibility than laypeople) and are often less risk-tolerant than financial professionals. The method of sampling that the researcher chose is known as purposeful sampling. According to Easton & McColl, it (...) is a fundamental sampling strategy where the researcher chooses a smaller group of people (a sample) from a larger group (a population) to study. The responders of the survey included both commissioned and non-commissioned PNP members. There were 67 non-commissioned individuals and 33 commissioned personnel. These individuals are at work while this study is being done. Because they think mutual funds can invest in a variety of assets in the future, think savings account interest is higher than that on fixed deposit accounts, and even budget their monthly income for expenses, savings, and investments, survey participants can be inferred to rate their level of financial knowledge as extremely high. Additionally, people learn about investments from their peers and think that growing costs will limit purchasing power. They also get banking advice from dependable friends. Because they want to make sure that they are not concerned about loans as long as their savings are protected and they refrain from purchasing non-essentials, police responders are persuaded that they have a very high attitude toward their finances. They also believe that investing in a business is a better use of their money than spending it now, that saving money over time is more satisfying than spending it now, and that their financial status is a big problem or a source of stress. The poll participants believe that their level of financial conduct is pretty high since the separate their needs and wants, have begun saving for their retirement, and consistently save money each pay period. They also budget their money, regulate their spending, and always pay their bills on time. (shrink)

Philosophy begins and ends in disagreement. Philosophers disagree among themselves in innumerable ways, and this pervasive and permanent dissent is a sign of their inability to solve philosophical problems and establish substantive truths. This raises the question: What should I do with my philosophical beliefs in light of philosophy's epistemic failure? In this open-access book, János Tozsér develops four possible answers into comprehensive metaphilosophical visions and argues that we cannot find peace either by committing ourselves to one of these visions (...) or by abandoning our philosophical beliefs. Tozsér calls this disheartening insight "the experience of breakdown", making the case that philosophy's truth-seeking and justificatory tools are unsuitable for establishing truths. Shedding light on the epistemic status of our philosophical beliefs, Tozsér adopts a broad approach to assess the metaphilosophical visions used by philosophers to interpret their own activity and determine their epistemic self-esteem. Open access was funded by the Research Centre for the Humanities, Hungary. (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.

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)

(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)

chapter on perception and bias including implicit bias.

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)

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)

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)

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)

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)

This article tries to show that commonplace economic, ethico-religious, anti-racist,and logical-consistency objections to public funding of abortions and abortion counseling for poor women are quite weak. By contrast, arguments appealing to basic human rights to freedom of speech, informed consent, protection from great harm, justice and equal protection under the law, strongly support public funding. Thus, refusing to provide abortions at public expense for women who cannot afford them is morally unacceptable and rationally unjustifiable, despite the opinions of (...) former Presidents Reagan and Bush, the more conservative members of the Supreme Court of the United States, the current Congress, and the majority of the American people. (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)

Most of us are hedonically future-biased: other things being equal, we prefer pains to be in the past and pleasures to be in the future. Recently, various authors have argued that future bias is irrational, and that we should be temporally neutral instead. I argue that instead of temporal neutrality, the putative counterexamples and the rationales offered for them only motivate a more narrow principle I call Only Action Fixes Utility: it is only when you act on the basis (...) of assigning a utility to an outcome that rationality requires you to give it the same value retrospectively and prospectively, other things being equal. When hedonic experiences are untethered from action, hedonic future bias is rationally permissible. I support this principle by appeal to additional scenarios and more general asymmetries between agential and experiential goods. (shrink)

We introduce the Bias Network Approach (BNA) as a sociotechnical method for AI developers to identify, map, and relate biases across the AI development process. This approach addresses the limitations of what we call the "isolationist approach to AI bias," a trend in AI literature where biases are seen as separate occurrence linked to specific stages in an AI pipeline. Dealing with these multiple biases can trigger a sense of excessive overload in managing each potential bias individually (...) or promote the adoption of an uncritical approach to understanding the influence of biases in developers’ decision-making. The BNA fosters dialogue and a critical stance among developers, guided by external experts, using graphical representations to depict biased connections. To test the BNA, we conducted a pilot case study on the "waiting list” project, involving a small AI developer team creating a healthcare waiting list NPL model in Chile. The analysis showed promising findings: (i) the BNA aids in visualizing interconnected biases and their impacts, facilitating ethical reflection in a more accessible way; (ii) it promotes transparency in decision-making throughout AI development; and (iii) more focus is necessary on professional biases and material limitations as sources of bias in AI development. (shrink)

In analyzing implicit bias, one key issue is to clarify its metaphysical nature. In this paper, I develop a novel account of implicit bias by highlighting a particular kind of belief-like state that is partly constituted by phenomenal experiences. I call these states ‘qualiefs’ for three reasons: qualiefs draw upon qualitative experiences of what an object seems like to attribute a property to this very object, they share some of the distinctive features of proper beliefs, and they also (...) share some characteristics of what Gendler calls ‘aliefs’. I proceed as follows: First, I develop a general theory of qualiefs. Second, I argue that implicit bias involves generic qualiefs that involve experiences that have been shaped by stereotypes. Elaborating on the particular content of a generic qualief, I explain why we are unaware of the bias even though it involves an experience. Third, I demonstrate that the qualief-model best explains the key features of implicit bias: it accounts for the biases´ implicitness and automaticity. Moreover, it elucidates how implicit bias can be insensitive to logical form and evidence, but at the same time it can serve as propositional input to further mental states. (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)

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)

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)

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)

Future-bias is the preference, all else being equal, for positive states of affairs to be located in the future not the past, and for negative states of affairs to be located in the past not the future. Three explanations for future-bias have been posited: the temporal metaphysics explanation, the practical irrelevance explanation, and the three mechanisms explanation. Understanding what explains future-bias is important not only for better understanding the phenomenon itself, but also because many philosophers think that (...) which explanation is the correct one has implications for our evaluation of the normative status of future-bias. In this paper we focus on the connection between future-bias and the temporal value asymmetry. The latter refers to the fact that people tend to assign more value to a state of affairs when it is located in the future, as opposed to being equidistant in the past. All three candidate explanations offered for future-bias proceed by explaining the temporal value asymmetry, and then supposing that future-bias is simply a manifestation of that asymmetry. Recently, however, it has been argued that the conditions under which people display the temporal value asymmetry and future-bias are different, and that this is reason to doubt that the latter is a manifestation of the former. In this paper we empirically test one response to this argument against the manifestation thesis—the simulation response—according to which once we control for the degree to which people can simulate the object of their preference, we find that people display future-bias and the temporal value asymmetry in the same conditions. We find no evidence in favour of the simulation response. Moreover, our results support some previous research that suggests that the temporal value asymmetry is less robust than previously thought, and this in itself casts doubt on the idea that future bias is just a manifestation of this asymmetry. (shrink)

Present-bias is the preference, all else being equal, for positive events to be located in the present rather than the non-present, and for negative events to be located in the non-present rather than the present. Very little attention has been given to present-bias in the contemporary literature on time biases. This may be because it is often assumed that present-bias is not a distinctive psychological kind; that what explains people’s being present-biased is just what explains them displaying (...) various other time-biases. According to this view, there is no need to investigate present-bias independently of investigating these other biases, since present-bias is really just a manifestation of these other biases. We call this the manifestation thesis. We take up the question of whether the manifestation thesis is true, and argue that it is not. Thus, by failing to investigate present-bias in its own right we are failing fully to understand the spectrum of ways in which people display time biases. In turn, we suggest, this may have implications for the ways we evaluate whether present-bias is rationally permissible. (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 concept of bias is pervasive in both popular discourse and empirical theorizing within philosophy, cognitive science, and artificial intelligence. This widespread application threatens to render the concept too heterogeneous and unwieldy for systematic investigation. This article explores recent philosophical literature attempting to identify a single theoretical category—termed ‘bias’—that could be unified across different contexts. To achieve this aim, the article provides a comprehensive review of theories of bias that are significant in the fields of philosophy of (...) mind, cognitive science, machine learning, and epistemology. It focuses on key examples such as perceptual bias, implicit bias, explicit bias, and algorithmic bias, scrutinizing their similarities and differences. Although these explorations may not conclusively establish the existence of a natural theoretical kind, pursuing the possibility offers valuable insights into how bias is conceptualized and deployed across diverse domains, thus deepening our understanding of its complexities across a wide range of cognitive and computational processes. (shrink)

Humans typically display hindsight bias. They are more confident that the evidence available beforehand made some outcome probable when they know the outcome occurred than when they don't. There is broad consensus that hindsight bias is irrational, but this consensus is wrong. Hindsight bias is generally rationally permissible and sometimes rationally required. The fact that a given outcome occurred provides both evidence about what the total evidence available ex ante was, and also evidence about what that evidence (...) supports. Even if you in fact evaluate the ex ante evidence correctly, you should not be certain of this. Then, learning the outcome provides evidence that if you erred, you are more likely to have erred low rather than high in estimating the degree to which the ex ante evidence supported the hypothesis that that outcome would occur. (shrink)

What is the status of research on implicit bias? In light of meta‐analyses revealing ostensibly low average correlations between implicit measures and behavior, as well as various other psychometric concerns, criticism has become ubiquitous. We argue that while there are significant challenges and ample room for improvement, research on the causes, psychological properties, and behavioral effects of implicit bias continues to deserve a role in the sciences of the mind as well as in efforts to understand, and ultimately (...) combat, discrimination and inequality. (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)

Future-bias is preferring some lesser future good to a greater past good because it is in the future, or preferring some greater past pain to some lesser future pain because it is in the past. Most of us think that this bias is rational. I argue that no agents have future-biased preferences that are rationally evaluable—that is, evaluable as rational or irrational. Given certain plausible assumptions about rational evaluability, either we must find a new conception of future-bias (...) that avoids the difficulties I raise, or we must conclude that future-biased preferences are not subject to rational evaluation. (shrink)

The underrepresentation of women, people of color, and especially women of color—and the corresponding overrepresentation of white men—is more pronounced in philosophy than in many of the sciences. I suggest that part of the explanation for this lies in the role played by the idealized rational self, a concept that is relatively influential in philosophy but rarely employed in the sciences. The idealized rational self models the mind as consistent, unified, rationally transcendent, and introspectively transparent. I hypothesize that acceptance of (...) the idealized rational self leads philosophers to underestimate the influence of implicit bias on their own judgments and prevents them from enacting the reforms necessary to minimize the effects of implicit bias on institutional decision-making procedures. I consider recent experiments in social psychology that suggest that an increased sense of one’s own objectivity leads to greater reliance on bias in hiring scenarios, and I hypothesize how these results might be applied to philosophers’ evaluative judgments. I discuss ways that the idealized rational self is susceptible to broader critiques of ideal theory, and I consider some of the ways that the picture functions as a tool of active ignorance and color-evasive racism. (shrink)

We argue that work on norms provides a way to move beyond debates between proponents of individualist and structuralist approaches to bias, oppression, and injustice. We briefly map out the geography of that debate before presenting Charlotte Witt’s view, showing how her position, and the normative ascriptivism at its heart, seamlessly connects individuals to the social reality they inhabit. We then describe recent empirical work on the psychology of norms and locate the notions of informal institutions and soft structures (...) with respect to it. Finally, we argue that the empirical resources enrich Witt’s ascriptivism, and that the resulting picture shows theorists need not, indeed should not, choose between either the individualist or structuralist camp. (shrink)

Private funding of life sciences has been extensively criticized as lacking objectivity (e.g., Bekelman et al. 2003). However, it is also important to point out that public funding of life sciences faces many objections. In order to improve the system of publicly funded life sciences and its ability to respond to global health challenges such as the COVID-19 pandemic, we should focus on several aspects. First of all, providing existential stability for researchers, in turn, could result in the (...) decrease of academic misconduct. Secondly, COVID-19 vaccines come from all parts of the world and have to be distributed throughout the world, which is a strong argument in favor of the positive stimulation of global science conducted at less-known research institutions and throughout all countries. This brings us to the quest of working on epistemic decolonization and inclusion in contrast to the current elitist paradigm in science. Finally, publicly funded research, which is at the moment mainly focused on foundational questions, should also be extended to applied ones. Public funding of applied research would lead to its development independently of private financial interests. (shrink)

It has widely been assumed, by philosophers, that our first-person preferences regarding pleasurable and painful experiences exhibit a bias toward the future (positive and negative hedonic future-bias), and that our preferences regarding non-hedonic events (both positive and negative) exhibit no such bias (non-hedonic time-neutrality). Further, it has been assumed that our third-person preferences are always time-neutral. Some have attempted to use these (presumed) differential patterns of future-bias—different across kinds of events and perspectives—to argue for the irrationality (...) of hedonic future-bias. This paper experimentally tests these descriptive hypotheses. While as predicted we found first-person hedonic future-bias, we did not find that participants were time-neutral in all other conditions. Hence, the presumed asymmetry of hedonic/non-hedonic and first/third-person preferences cannot be used to argue for the irrationality of future-bias, since no such asymmetries exist. Instead, we develop a more fine-grained approach, according to which three factors—positive/negative valence, first/third-person, and hedonic/non-hedonic—each independently influence, but do not determine, whether an event is treated in a future-biased or time-neutral way. We discuss the upshots of these results for the debate over the rationality of future-bias. (shrink)

Patient-funded trials are gaining traction as a means of accelerating clinical translation. However, such trials sidestep mechanisms that promote rigor, relevance, efficiency, and fairness. We recommend that funding bodies or research institutions establish mechanisms for merit review of patient-funded trials, and we offer some basic criteria for evaluating PFT protocols.

In recent years, a disagreement has erupted between two camps of philosophers about the rationality of bias toward the near and bias toward the future. According to the traditional hybrid view, near bias is rationally impermissible, while future bias is either rationally permissible or obligatory. Time neutralists, meanwhile, argue that the hybrid view is untenable. They claim that those who reject near bias should reject both biases and embrace time neutrality. To date, experimental work has (...) focused on future-directed near bias. The primary aim of this paper is to shed light on the debate by investigating past-directed near bias. If people treat the past and future differently with respect to near bias, by being future-directed but not past-directed near biased, then this supports a particular version of the hybrid view: temporal metaphysic hybridism. If people treat the past and future the same with respect to near bias, then this supports a simple version of time neutralism, which explains both future bias and near bias in terms of the functioning of a single mechanism: the anticipatory/retrospectory mechanism. Our results undermine the claim that people are future-directed, but not past-directed, near biased, and hence do not support temporal metaphysic hybridism. They also fail to support simple time-neutralism; instead, they suggest that there are multiple mechanisms that differently shape future- and past-directed preferences. (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)

To what extent should we focus on implicit bias in order to eradicate persistent social injustice? Structural prioritizers argue that we should focus less on individual minds than on unjust social structures, while equal prioritizers think that both are equally important. This article introduces the framework of transactive memory into the debate to defend the equal priority view. The transactive memory framework helps us see how structure can emerge from individual interactions as an irreducibly social product. If this is (...) right, then debiasing interventions are structural interventions. One upshot is that the utility of the individual versus structural distinction is not apparent for the purposes of intervention. (shrink)