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)

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)

Future-bias is the preference, all else being equal, for negatively valenced events be located in the past rather than the future, and positively valenced ones to be located in the future rather than the past. Strong risk aversion is the preference to pay some cost to mitigate the badness of the worst outcome. People who are both strongly risk averse and future-biased can face a series of choices that will guarantee them more pain, for no compensating benefit: (...) they will be pain pumped. Thus, combining these preferences is rationally impermissible. Dougherty (2011) argues that this gives us reason to think that future-bias is rationally impermissible. This argument, and a similar one presented by Greene and Sullivan (2015), relies on the idea that if several preferences are not rationally combinable, then in the absence of some explanation of why they are not combinable, we should conclude that (at least) one of them is rationally impermissible. We take up this question by, inter alia, reflecting on the empirical results of a study we ran that probes people’s risk averse and future-biased preferences. We argue that the data, in conjunction with other considerations, suggests that we should not infer from the irrationality of the combination of these preferences to the irrationality of one of the preferences singly. We also argue that given the descriptive data about these preferences, there is no reason to think that, if one of them is rationally impermissible, then it is future-bias. (shrink)

Risk attitude is known to be a key determinant of various economic and financial choices. Behavioral studies that aim to evaluate the role of risk attitudes in contexts of this type, therefore, require tools for measuring individual risk tolerance. Recent developments in decision theory provide such tools. However, the methods available can be time consuming. As a result, some practitioners might have an incentive to prefer “fast and frugal” methods to clean but more costly methods. In this (...) article, we focus on a tractable procedure initially proposed by Holt and Laury (2002) to elicit risk attitude. We generalize this method to measure utility and risk aversion as follows. First, we allow measurement of probabilistic risk attitude through violations of expected utility due to probability weighting. Second, we use the outcome scale rather than the probability scale in the menu of choices. Third, we compare sure payoffs with lotteries instead of comparing non-degenerate lotteries. A within-subject experimental study illustrates the gains in tractability and bias minimization that can result from such an extension. (shrink)

Empirical data can be an extremely powerful and influential tool in bioethical research. However, when researchers or policy makers look for answers to ethical questions by engaging with empirical research, there can be a tendency (conscious or unconscious) to shape, report, and use empirical research in a way that confirms their own preferred ethical conclusions. This skewing effect - what we call ‘normative bias’ - is often so subtle it falls short of clear misconduct and thus can be difficult (...) to call out. However, we argue that this subtle influence of bias has the potential to significantly influence debate and policy around highly sensitive ethical issues and must be guarded against. In this paper we share the lessons we have learned through a journey of self-reflection around the effect that normative bias can have when reporting on and referring to empirical data relating to ethical issues. We use a variety of papers from our area of the ethics of routine prenatal screening to illustrate these subtle but often powerfully distorting effects of bias. Our aim in doing so is not to criticise the work of others, as we recognise our own normative bias, but to improve awareness of this issue, remind the need for reflexivity to guard against our own biases, and introduce a new criterion - the idea of a ‘limitation prominence assessment’ - that can work as a practical way to evaluate the seriousness of the limitations of an empirical study and thus, the risks of the study being misread or misinterpreted through superficial reading. (shrink)

Various experimental procedures aimed at measuring individual risk aversion involve a list of pairs of alternative prospects. We first study the widely used method by Holt and Laury :1644–1655, 2002), for which we find that the removal of some items from the lists yields a systematic decrease in risk aversion and scrambles the ranking of individuals by risk aversion. This bias, that we call embedding bias, is quite distinct from other confounds that have been previously (...) observed in the use of the HL method. It may be related to empirical phenomena and theoretical developments where better prospects increase risk aversion. Nevertheless, we also find that the more recent elicitation method due to Abdellaoui et al., also based on lists but using only one and the same probability in the list, does not display any statistically significant bias when the corresponding items of the list are removed. Our results suggest that methods other than the popular HL one may be preferable for the measurement of risk aversion. (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)

The commodity market plays a vital role in boosting the economy. Investors make decisions based on market knowledge and ignore cognitive biases. These cognitive biases or judgment errors have a significant effect on investment decisions. Therefore, this study aimed to investigate the effect of emotional intelligence on decision-making. In addition, optimism bias and risk perception are the intervening variables between emotional intelligence and decision-making. So, this study contributes to the body of knowledge by examining the mediating role of (...) optimism bias and risk perception. The data were collected from the respondents of the commodity market and the 370 questionnaires were distributed among the investors, of which 337 respondents gave their feedback. The convenience base sampling technique is used due to the easy access of respondents, time factor, and cost factor. Data entered into the SPSS Statistics Version 26 and PROCESS macro model 6 were used for serial mediation. AMOS was used for the validity and model-fit analysis. The results of this study aligned with the literature that there is a significant effect of emotional intelligence on decision-making. It also observed that optimism bias has a positive effect on decision-making. The finding of this study will be helpful for the brokers, the government, and especially the investors. This study also proposed that future studies on the stock exchange and real estate market comparative analysis can be conducted. (shrink)

Previous research on moral dilemmas has mainly focused on decisions made under conditions of probabilistic certainty. We investigated the impact of uncertainty on the preference for action (killing one individual to save five people) and inaction (saving one but allowing five people to die) in moral dilemmas. We reported two experimental studies that varied the framing (gain vs loss), levels of risk (probability of gain and loss) and levels of ambiguity (imprecise probability information) in the choice to save five (...) individuals by sacrificing one. We found that participants preferred actions with uncertainty (risk/ambiguity) over inaction. Specifically, we found that participants preferred actions with precise probability information (risk) over inaction, and they preferred actions with modest or high levels of ambiguity over actions with precise probabilities, especially when moral dilemmas had a loss frame. We also observed commission bias in Study 2. We discussed the implications for research in moral decision-making. (shrink)

Behavioral research has revealed how normal human cognitive processes can tend to lead us astray. But do these affect economic researchers, ourselves? This article explores the consequences of stereotyping and confirmation bias using a sample of published articles from the economics literature on gender and risk aversion. The results demonstrate that the supposedly ‘robust’ claim that ‘women are more risk averse than men’ is far less empirically supported than has been claimed. The questions of how these cognitive (...) biases arise and why they have such power are discussed, and methodological practices that may help to attenuate these biases are outlined. (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)

Often machine learning programs inherit social patterns reflected in their training data without any directed effort by programmers to include such biases. Computer scientists call this algorithmic bias. This paper explores the relationship between machine bias and human cognitive bias. In it, I argue similarities between algorithmic and cognitive biases indicate a disconcerting sense in which sources of bias emerge out of seemingly innocuous patterns of information processing. The emergent nature of this bias obscures the (...) existence of the bias itself, making it difficult to identify, mitigate, or evaluate using standard resources in epistemology and ethics. I demonstrate these points in the case of mitigation techniques by presenting what I call ‘the Proxy Problem’. One reason biases resist revision is that they rely on proxy attributes, seemingly innocuous attributes that correlate with socially-sensitive attributes, serving as proxies for the socially-sensitive attributes themselves. I argue that in both human and algorithmic domains, this problem presents a common dilemma for mitigation: attempts to discourage reliance on proxy attributes risk a tradeoff with judgement accuracy. This problem, I contend, admits of no purely algorithmic solution. (shrink)

Many philosophers think that we should use a lottery to decide who gets a good to which two persons have an equal claim but which only one person can get. Some philosophers think that we should save identified persons from harm even at the expense of saving a somewhat greater number of statistical persons from the same harm. I defend a principled way of justifying both judgements, namely, by appealing to the decreasing marginal moral value of survival chances. I identify (...) four desiderata that, I contend, any such justification should satisfy, and explain how my account meets these desiderata, unlike some previous accounts. (shrink)

In this article, I describe how gender bias can affect the design, testing, clinical trials, regulatory approval, and clinical use of implantable devices. I argue that bad outcomes experienced by women patients are a cumulative consequence of small biases and inattention at various points of the design, testing, and regulatory process. However, specific instances of inattention and bias can be difficult to identify, and risks are difficult to predict. This means that even if systematic gender bias in (...) implant design is an ethical issue, it is one with no clearly blameworthy player. From a practical perspective, there is no single obvious point at which to intervene. Philosophers working in other areas have explored structurally similar moral problems—sometimes referred to as “moral aggregation problems”—such as the type of environmental harm caused by small actions of many players. I describe key features of these type of problems and strategies to address them. I then draw on these to suggest an approach to gender bias in medical implant design and use. (shrink)

Motivating reforms to address discrimination and exclusion is important. But what epistemic practices characterize better or worse ways of doing this? Recently, the phenomena of implicit biases have played a large role in motivating reforms. We argue that this strategy risks perpetuating two kinds of epistemic oppression: the vindication dynamic and contributory injustice. We offer positive proposals for avoiding these forms of epistemic oppression when confronting racism.

The purpose of this research is to empirically test the salmon bias hypothesis, which states that the “healthy migrant” effect—referring to a situation in which migrants enjoy lower mortality risks than natives—is caused by selective return-migration of the weak, sick, and elderly. Using a unique longitudinal micro-level database—the Historical Sample of the Netherlands—we tracked the life courses of internal migrants after they had left the city of Rotterdam, which allowed us to compare mortality risks of stayers, returnees, and movers (...) using survival analysis for the study group as a whole, and also for men and women separately. Although migrants who stayed in the receiving society had significantly higher mortality risks than natives, no significant difference was found for migrants who returned to their municipality of birth. By contrast, migrants who left for another destination had much lower mortality risks than natives. Natives who left Rotterdam also had significantly lower mortality risks than natives who stayed in Rotterdam. Female migrants, in particular, who stayed in the receiving urban society paid a long-term health price. In the case of Rotterdam, the salmon bias hypothesis can be rejected because the lower mortality effect among migrants was not caused by selective return-migration. The healthy migrant effect is real and due to a positive selection effect: Healthier people are more likely to migrate. (shrink)

The use of advanced AI and data-driven automation in the public sector poses several organisational, practical, and ethical challenges. One that is easy to underestimate is automation bias, which, in turn, has underappreciated legal consequences. Automation bias is an attitude in which the operator of an autonomous system will defer to its outputs to the point where the operator overlooks or ignores evidence that the system is failing. The legal problem arises when statutory office-holders (or their employees) either (...) fetter their discretion to in-house algorithms or improperly delegate their discretion to third-party software developers—something automation bias may facilitate. A synthesis of previous research suggests an easy way to mitigate the risks of automation bias and its potential legal ramifications is for those responsible for procurement decisions to adhere to a simple checklist that ensures that the pitfalls of automation are avoided as much as possible. (shrink)

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

Special Issue “Risks of artificial general intelligence”, Journal of Experimental and Theoretical Artificial Intelligence, 26/3 (2014), ed. Vincent C. Müller. http://www.tandfonline.com/toc/teta20/26/3# - Risks of general artificial intelligence, Vincent C. Müller, pages 297-301 - Autonomous technology and the greater human good - Steve Omohundro - pages 303-315 - - - The errors, insights and lessons of famous AI predictions – and what they mean for the future - Stuart Armstrong, Kaj Sotala & Seán S. Ó hÉigeartaigh - pages 317-342 - - (...) - The path to more general artificial intelligence - Ted Goertzel - pages 343-354 - - - Limitations and risks of machine ethics - Miles Brundage - pages 355-372 - - - Utility function security in artificially intelligent agents - Roman V. Yampolskiy - pages 373-389 - - - GOLEM: towards an AGI meta-architecture enabling both goal preservation and radical self-improvement - Ben Goertzel - pages 391-403 - - - Universal empathy and ethical bias for artificial general intelligence - Alexey Potapov & Sergey Rodionov - pages 405-416 - - - Bounding the impact of AGI - András Kornai - pages 417-438 - - - Ethics of brain emulations - Anders Sandberg - pages 439-457. (shrink)

The reasonable person standard is used in adjudicating claims of self-defence. In US law, an individual may use defensive force if her beliefs that a threat is imminent and that force is required are beliefs that a reasonable person would have. In English law, it is sufficient that beliefs in imminence and necessity are genuinely held; but the reasonableness of so believing is given an evidential role in establishing the genuineness of the beliefs. There is, of course, much contention over (...) how to spell out when, and in virtue of what, such beliefs are reasonable. In this chapter, we identify some distinctive issues that arise when we consider that implicit racial bias might be implicated in the beliefs in imminence and necessity. Considering two prominent interpretations of the reasonable person standard, we argue that neither is acceptable. On one interpretation, we risk unfairness to the defendant-who may non-culpably harbour bias. On another, the standard embeds racist stereotypes. Whilst there are formulations of the defence that may serve to mitigate these problems, we argue that they cannot be avoided in the presence of racist social structures. (shrink)

The concept of equipoise is considered by many to be part of the ethical justification for using human subjects in clinical research. In general, equipoise indicates some uncertainty about the relative merits of the experimental intervention compared to existing treatments. Relieving this uncertainty gives scientific value to an experiment, thereby making the risks to human subjects in the trial acceptable, other considerations notwithstanding. But characterizing equipoise remains controversial since Freedman’s groundbreaking publication on the subject. We offer a new account of (...) equipoise that draws on and extends an option Freedman discarded. After establishing the importance of some account of equipoise as part of ethically justified clinical trials, we revisit Freedman’s distinction between clinical and theoretical equipoise. We raise concerns about Freedman’s preferred clinical equipoise and then rehabilitate theoretical equipoise. In particular, we use a variety of arguments from epistemology to show how Freedman was too hasty in rejecting theoretical equipoise. In addition, we argue that theoretical equipoise is a subspecies of epistemic equipoise, which we characterize as a form of uncertainty that is the result of the possibility of error. This type of uncertainty can best be alleviated by research, which produces beliefs supported by strong statistical evidence, which is a key aim of clinical trials. Further, this type of uncertainty can explain why even clinicians with more firsthand experience with an intervention than their peers, and who may not be in theoretical equipoise, could still justifiably support a trial designed to ameliorate the possibility of error due to cognitive bias. (shrink)

As algorithms have become ubiquitous in consequential domains, societal concerns about the potential for discriminatory outcomes have prompted urgent calls to address algorithmic bias. In response, a rich literature across computer science, law, and ethics is rapidly proliferating to advance approaches to designing fair algorithms. Yet computer scientists, legal scholars, and ethicists are often not speaking the same language when using the term ‘bias.’ Debates concerning whether society can or should tackle the problem of algorithmic bias are (...) hampered by conflations of various understandings of bias, ranging from neutral deviations from a standard to morally problematic instances of injustice due to prejudice, discrimination, and disparate treatment. This terminological confusion impedes efforts to address clear cases of discrimination. -/- In this paper, we examine the promises and challenges of different approaches to disambiguating bias and designing for justice. While both approaches aid in understanding and addressing clear algorithmic harms, we argue that they also risk being leveraged in ways that ultimately deflect accountability from those building and deploying these systems. Applying this analysis to recent examples of generative AI, our argument highlights unseen dangers in current methods of evaluating algorithmic bias and points to ways to redirect approaches to addressing bias in generative AI at its early stages in ways that can more robustly meet the demands of justice. (shrink)

Some experts have argued that patients facing certain types of choices should not be told whether their risk is above or below average, because this information may trigger a bias (Fagerlin et al. 2007). But careful consideration shows that the comparative risk heuristic can usefully guide decisions and improve their quality or rationality. Building on an earlier paper of mine (Schwartz 2009), I will argue here that doctors and decision aids should provide comparative risk information to (...) patients, even while further research is conducted. (shrink)

Papers from the conference on AI Risk (published in JETAI), supplemented by additional work. --- If the intelligence of artificial systems were to surpass that of humans, humanity would face significant risks. The time has come to consider these issues, and this consideration must include progress in artificial intelligence (AI) as much as insights from AI theory. -- Featuring contributions from leading experts and thinkers in artificial intelligence, Risks of Artificial Intelligence is the first volume of collected chapters dedicated (...) to examining the risks of AI. The book evaluates predictions of the future of AI, proposes ways to ensure that AI systems will be beneficial to humans, and then critically evaluates such proposals. 1 Vincent C. Müller, Editorial: Risks of Artificial Intelligence - 2 Steve Omohundro, Autonomous Technology and the Greater Human Good - 3 Stuart Armstrong, Kaj Sotala and Sean O’Heigeartaigh, The Errors, Insights and Lessons of Famous AI Predictions - and What they Mean for the Future - 4 Ted Goertzel, The Path to More General Artificial Intelligence - 5 Miles Brundage, Limitations and Risks of Machine Ethics - 6 Roman Yampolskiy, Utility Function Security in Artificially Intelligent Agents - 7 Ben Goertzel, GOLEM: Toward an AGI Meta-Architecture Enabling Both Goal Preservation and Radical Self-Improvement - 8 Alexey Potapov and Sergey Rodionov, Universal Empathy and Ethical Bias for Artificial General Intelligence - 9 András Kornai, Bounding the Impact of AGI - 10 Anders Sandberg, Ethics and Impact of Brain Emulations 11 Daniel Dewey, Long-Term Strategies for Ending Existential Risk from Fast Takeoff - 12 Mark Bishop, The Singularity, or How I Learned to Stop Worrying and Love AI -. (shrink)

A central task for clinical ethics consultants and committees (CEC) is providing analysis of, and advice on, prospective or retrospective clinical cases. However, several kinds of biases may threaten the integrity, relevance or quality of the CEC's deliberation. Bias should be identified and, if possible, reduced or counteracted. This paper provides a systematic classification of kinds of bias that may be present in a CEC's case deliberation. Six kinds of bias are discussed, with examples, as to their (...) significance and risk factors. Possible remedies are suggested. The potential for bias is greater when the case deliberation is performed by an individual ethics consultant than when an entire clinical ethics committee is involved. (shrink)

While rapid advances in artificial intelligence hiring tools promise to transform the workplace, these algorithms risk exacerbating existing biases against marginalized groups. In light of these ethical issues, AI vendors have sought to translate normative concepts such as fairness into measurable, mathematical criteria that can be optimized for. However, questions of disability and access often are omitted from these ongoing discussions about algorithmic bias. In this paper, I argue that the multiplicity of different kinds and intensities of people’s (...) disabilities and the fluid, contextual ways in which they manifest point to the limits of algorithmic fairness initiatives. In particular, existing de-biasing measures tend to flatten variance within and among disabled people and abstract away information in ways that reinforce pathologization. While fair machine learning methods can help mitigate certain disparities, I argue that fairness alone is insufficient to secure accessible, inclusive AI. I then outline a disability justice approach, which provides a framework for centering disabled people’s experiences and attending to the structures and norms that underpin algorithmic bias. (shrink)

A decision maker bets on the outcomes of a sequence of coin-tossings. At the beginning of the game the decision maker can choose one of two coins to play the game. This initial choice is irreversible. The coins can be biased and the player is uncertain about the nature of one (or possibly both) coin(s). If the player is an expected-utility maximizer, her choice of the coin will depend on different elements: the nature of the game (namely, whether she can (...) observe the outcomes of the previous tosses before making her next decision), her utility function, the prior distribution on the bias of the coin. We will show that even a risk averter might optimally choose a riskier coin when learning is allowed. We will express most of our results in the language of stochastic orderings, allowing comparisons that are valid for large classes of utility functions. (shrink)

The vegan ideal is entailed by arguments for ethical veganism based on traditional moral theory (rights and/or utilitarianism) extended to animals. The most ideal lifestyle would abjure the use of animals or their products for food since animals suffer and have rights not to be killed. The ideal is discriminatory because the arguments presuppose a male physiological norm that gives a privileged position to adult, middle-class males living in industrialized countries. Women, children, the aged, and others have substantially different nutritional (...) requirements and would bear a greater burden on vegetarian and vegan diets with respect to health and economic risks, than do these males. The poor and many persons in Third World nations live in circumstances that make the obligatory adoption of such diets, where they are not already a matter of sheer necessity, even more risky.Traditional moral theorists (such as Evelyn Pluhar and Gary Varner whose essays appear in this issue) argue that those who are at risk would beexcused from a duty to attain the virtue associated with ethical vegan lifestyles. The routine excuse of nearly everyone in the world besides adult, middle-class males in industrialized countries suggests bias in the perspective from which traditional arguments for animal rights and (utilitarian) animal welfare are formulated. (shrink)

The shooting bias hypothesis aims to explain the disproportionate number of minorities killed by police. We present the evidence mounting in support of the existence of shooting bias and then focus on two dissenting studies. We examine these studies in light of Biddle and Leuschner’s “inductive risk account of epistemically detrimental dissent” and conclude that, although they meet this account only partially, the studies are in fact epistemically and socially detrimental as they contribute to racism in society (...) and to a social atmosphere that is hostile to science as scholars working on issues of racism come under attack. We emphasize this final point via recourse to Kitcher’s “Millian argument against the freedom of research.”. (shrink)

One important strategy for dealing with error in our methods is triangulation, or the use multiple methods to investigate the same object. Current accounts of triangulation assume that its primary function is to provide a confirmatory boost to hypotheses beyond what confirmation of each method alone could produce. Yet, researchers often use multiple methods to examine new constructs about which they are uncertain. For example, social psychologists use multiple indirect measures to provide convergent evidence about implicit attitudes, but how to (...) characterize implicit attitudes is an open question. To make sense of triangulation under uncertainty about constructs, I suggest two changes: first, triangulation can serve multiple epistemic functions, including some that are non-confirmatory, and second, researchers should assess the epistemic risk in claims about evidence and the acceptance/rejection of hypotheses. (shrink)

The use of social media is becoming a necessary daily activity in today’s society. Excessive and compulsive use of social media may lead to social media addiction. The main aim of this study was to investigate whether demographic factors, impulsivity, self-esteem, emotions, and attentional bias were risk factors associated with SMA. The study was conducted in a non-clinical sample of college students, ranging in age from 16 to 23 years, including 277 females and 243 males. All participants completed (...) a survey measuring impulsivity, self-esteem, anxiety, depression, social anxiety, loneliness, and attentional bias. The final hierarchical regression model indicated significant risk factors for SMA with an accuracy of 38%. The identified set of associated risk factors included female gender, impulsivity, self-esteem, anxiety, social anxiety, and negative attentional biases. Finally, a discussion of the results is presented, followed by corresponding recommendations for future studies. (shrink)

This article argues that the controversy over genetically modified crops is best understood not in terms of the supposed bias, dishonesty, irrationality, or ignorance on the part of proponents or critics, but rather in terms of differences in values. To do this, the article draws on and extends recent work of the role of values and interests in science, focusing particularly on inductive risk and epistemic risk, and it shows how the GMO debate can help to further (...) our understanding of the various epistemic risks that are present in science and how these risks might be managed. (shrink)

ABSTRACTMemory bias is a risk factor for depression. In two independent studies, the efficacy of one CBM-Memory session on negative memory bias and depressive symptoms was tested in vulnerable samples. We compared positive to neutral CBM-Memory trainings in highly-ruminating individuals and individuals with elevated depressive symptoms. In both studies, participants studied positive, neutral, and negative Swahili words paired with their translations. In five study–test blocks, they were then prompted to retrieve either only the positive or neutral translations. (...) Immediately following the training and one week later, we tested cued recall of all translations and autobiographical memory bias; and also measured mood, depressive symptoms, and rumination. Retrieval practice resulted in training-congruent recall both immediately after and one week after the training. Overall, there was no differential decrease in symptoms or difference in autobiographical memory bias between the tra... (shrink)

Risk taking is a component of the decision-making process in situations that involve uncertainty and in which the probability of each outcome – rewards and/or negative consequences – is already known. The influence of cognitive and emotional processes in decision making may affect how risky situations are addressed. First, inaccurate assessments of situations may constitute a perceptual bias in decision making, which might influence RT. Second, there seems to be consensus that a proneness bias exists, known as (...) risk proneness, which can be defined as the propensity to be attracted to potentially risky activities. In the present study, we take the approach that risk perception and risk proneness affect RT behaviours. The study hypothesises that locus of control, emotion regulation, and executive control act as perceptual biases in RT, and that personality, sensation seeking, and impulsivity traits act as proneness biases in RT. The results suggest that locus of control, emotion regulation and executive control influence certain domains of RT, while personality influences in all domains except the recreational, and sensation seeking and impulsivity are involved in all domains of RT. The results of the study constitute a foundation upon which to build in this research area and can contribute to the increased understanding of human behaviour in risky situations. (shrink)

ObjectivesThe purpose of this study was to identify risk factors for delirium after total joint arthroplasty and provide theoretical guidance for reducing the incidence of delirium after TJA.MethodsThe protocol for this meta-analysis is registered with PROSPERO. We searched PubMed, the Cochrane Library and Embase for observational studies on risk factors for delirium after TJA. Review Manager 5.3 was used to calculate the relative risk or standard mean difference of potential risk factors related to TJA. STATA 14.0 (...) was used for quantitative publication bias evaluation.ResultsIn total, 25 studies including 3,767,761 patients from 9 countries were included. Old age has been widely recognized as a risk factor for delirium. Our results showed that the main risk factors for delirium after TJA were patient factors, comorbidities, surgical factors and drug factors.ConclusionsMultiple risk factors were associated with delirium after TJA. These results may help doctors predict the occurrence of delirium after surgery and determine the correct treatment.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/, identifier: CRD42020170031. (shrink)

Student cheating has always been a problem in higher education, but detection of cheating has become easier with technology. As a result, more students are being caught and reported for cheating. While reporting cheating is not a negative, the rippling effects of reported cheating may be felt by some populations more than others. Thus, preventing cheating would be a preferable option for all involved.Identifying those at risk for being reported for cheating is a first step in developing preventive measures. (...) Previous research has attempted to do this through the use of self-report surveys, but such research takes considerable time and resources to conduct and suffers from low response rates and social desirability bias. To address this limitation in existing research, this study links existing data from records of other-reported cheating to university registration data in order to examine 6 cheating risk factors identified in previous research: maturity level, gender, grade point average, major, international student status, and fear of punishment. The results of the study suggest that international and transfer students, particularly those who are male, in high-risk majors, and have lower grade point averages, are in particular need of preventative education. Likewise, those faculty who teach in computer science, engineering and economics majors should do more to educate implement practices to reduce the likelihood of cheating. (shrink)

An unprocessed risk is a collection of simple lotteries with a reduction-rule that describes the actual-payoff to the decision-maker as a function of realized lottery outcomes. Experiments reveal that the willingness to pay for unprocessed risks is consistently biased toward the payoff-level in the unprocessed representation. The anchoring-to-frame bias in cases of positive framing is significantly weaker than in cases of negative framing suggesting that rational negativity bias may reflect in asymmetric violations of rationality.

Judges in multiple US states, such as New York, Pennsylvania, Wisconsin, California, and Florida, receive a prediction of defendants’ recidivism risk, generated by the COMPAS algorithm. If judges act on these predictions, they implicitly delegate normative decisions to proprietary software, even beyond the previously documented race and age biases. Using the ProPublica dataset, we demonstrate that COMPAS predictions favor jailing over release. COMPAS is biased against defendants. We show that this bias can largely be removed. Our proposed correction (...) increases overall accuracy, and attenuates anti-black and anti-young bias. However, it also slightly increases the risk that defendants are released who commit a new crime before tried. We argue that this normative decision should not be buried in the code. The tradeoff between the interests of innocent defendants and of future victims should not only be made transparent. The algorithm should be changed such that the legislator and the courts do make this choice. (shrink)

This paper analyzes a model of sequential parimutuel betting described as a two-horse race with a finite number of noise bettors and a finite number of strategic and symmetrically informed bettors. For generic objective probabilities that the favorite wins the race, a unique subgame perfect equilibrium is characterized. Additionally, two explanations for the favorite–longshot bias—according to which favorites win more often than the market's estimate of their winning chances imply—are offered. It is shown that this robust anomalous empirical regularity might (...) be due to the presence of transaction costs and/or to strategic bettors' subjective attitude to probabilities. (shrink)

In this paper we investigate risk prediction of criminal re-offense among juvenile defendants using general-purpose machine learning algorithms. We show that in our dataset, containing hundreds of cases, ML models achieve better predictive power than a structured professional risk assessment tool, the Structured Assessment of Violence Risk in Youth, at the expense of not satisfying relevant group fairness metrics that SAVRY does satisfy. We explore in more detail two possible causes of this algorithmic bias that are (...) related to biases in the data with respect to two protected groups, foreigners and women. In particular, we look at the differences in the prevalence of re-offense between protected groups and the influence of protected group or correlated features in the prediction. Our experiments show that both can lead to disparity between groups on the considered group fairness metrics. We observe that methods to mitigate the influence of either cause do not guarantee fair outcomes. An analysis of feature importance using LIME, a machine learning interpretability method, shows that some mitigation methods can shift the set of features that ML techniques rely on away from demographics and criminal history which are highly correlated with sensitive features. (shrink)

The chapter discusses two points of intersection between the communication of science-based information about risk and philosophical ethics. The first is a logically unnecessary bias toward consequentialist ethics, and a corresponding tendency to overlook the significance of deontological and virtue based ways to interpret the findings of a scientific risk analysis. The second is a grammatical bias that puts scientific communicators at odds with the expectations of a non-scientific audience.

In her excellent and comprehensive article, Friesen argues that utilising personal responsibility in healthcare is problematic in several ways: it is difficult to ascribe responsibility to behaviour; there is a risk of prejudice and bias in deciding which behaviours a person should be held responsible for; it may be ineffective at reducing health costs. In this short commentary, I will elaborate the critique of personal responsibility in health but suggest one way in which it could be used ethically. (...) In doing so, I will introduce the concepts of reasonable risk and golden opportunity. I previously argued that it is both difficult to disentangle responsibility and that we risk prejudice and bias in singling out behaviours that are socially disapproved of.1 So I am sympathetic to Friesen’s concerns. I also discussed another way in which ascribing personal responsibility for health is problematic which Friesen does not discuss and which, in my view, is the most concerning and further supports her arguments. It represents a back door assault on liberalism and neutrality towards concepts of the good life. Even if one were to accurately and in an unbiased way divine the personal contribution to disease, the greatest problem would remain: those who voluntarily take on risk would be penalised. That is, such a system would be risk-averse. Those lives which avoided risk would be prioritised, while those who chose to take on risky activities in their conception of the good life would be penalised. Yet risk is necessary, both for the good life, and social progress. Columbus, Edmund Hilary, Florence Nightingale, Ernest Hemingway and countless monumental figures in human history took risks in order to achieve something great. Should we aim for a society of trembling, teetotalling health nuts? Surely that prospect is horrific. It is horrific because it elevates health …. (shrink)

Conservative assumptions in medical ethics risk immense harms during a pandemic. Public health institutions and public discourse alike have repeatedly privileged inaction over aggressive medical interventions to address the pandemic, perversely increasing population-wide risks while claiming to be guided by ‘caution’. This puzzling disconnect between rhetoric and reality is suggestive of an underlying philosophical confusion. In this paper, I argue that we have been misled by status quo bias—exaggerating the moral significance of the risks inherent in medical interventions, (...) while systematically neglecting the risks inherent in the status quo prospect of an out-of-control pandemic. By coming to appreciate the possibility and significance of status quo risk, we will be better prepared to respond appropriately when the next pandemic strikes. (shrink)

The response mode bias, in which subjects exhibit different risk attitudes when assessing certainty equivalents versus indifference probabilities, is a well-known phenomenon in the assessment of utility functions. In this empirical study, we develop and apply a cardinal measure of risk attitudes to analyze not only the existence, but also the strength of this phenomenon. Since probability levels involved in decision problems are already known to have a strong impact on behavior, we use this approach to study (...) the impact of probabilities on the extent of the response mode bias. We find that the direction in which probabilities influence measured risk aversion is the opposite in the certainty equivalence (CE) method versus in the probability equivalence (PE) method. Utilizing the CE elicitation approach leads to an increase of risk seeking for gambles involving high probabilities. For the PE method, subjects tend to behave risk averse with gambles of high probabilities. This behavior is reversed in the gain domain. This “tailwhip” effect is consistently replicated in several experiments, involving both loss and gain domains of lotteries. (shrink)