There is no abstract for this chapter. The following is a summary. -/- We distinguish between, explicit, inadvertent, and habitual racist actions. We argue that while inadvertent bigots and habitual racists are inclined to (sincerely) deny that they committed a racially motivated action, they have different reasons for their denial. Inadvertent bigots are denying it because, however deeply they search, they are not going to find any such motive. Habitual racists, by contrast, may hold explicit egalitarian attitudes but they are (...) nonetheless implicitly racially motivated. There is thus an implicit stressful conflict between their covert racist intentions and their overt egalitarian attitudes. While they aren’t aware of the source of this distress, cognitive dissonance theory predicts that they nevertheless will engage in dissonance-reducing strategies. Specifically, we argue that habitual racists will be inclined to confabulate a description of the action in non-racial terms. (shrink)

A substantial portion of produce harvested around the world is wasted because it does not meet consumers’ shape expectations. Only recently has research begun investigating the causes underlying misshapen produce rejection by consumers. Generally, this limited research has concluded that misshapen produce is subject to an ugly penalty, leading consumers to form biased expectations regarding product attributes (e.g., healthiness, tastiness, or naturalness). In this research, we propose that this ugly penalty extends to the moral valuation of misshapen produce and that (...) this moral penalty is rooted in the feelings of disgust evoked by the visual appearance of such produce. Across five studies and two pilot studies, we show that misshapen fruits and vegetables are disgust elicitors, and that felt disgust causes consumers to make negative moral associations with produce which, in turn, leads to decreased preference. We also show that pairing misshapen produce with a positive unconditioned stimulus (i.e., an image of attractive individuals) is an efficient solution to counteract the effect of this stereotype. This research contributes to the growing body of work demonstrating the link between consumption behaviors, consumer preferences, and morality. (shrink)

What training should engineering students receive to enable them to contribute to reducing bias, discrimination and the persistent lack of diversity in engineering? Collaboration is central to professional engineering work and, consequently, teamwork and group projects are increasingly present in engineering curricula. However, the influence of unconscious bias on interactions within teams can negatively affect women and underrepresented groups and is now recognised as an important engineering ethics issue. This paper describes a workshop designed to enable engineering students to work (...) equitably in diverse teams. Key features of the workshop include (1) the emotionally safe, empowering and warm environment created, (2) the creation of opportunities for students to discuss and apply the issues raised to their own engineering projects, and (3) the opportunities to practice the use of both proactive and reactive strategies to address bias and discrimination in teams. The evaluation of the workshop suggests that engineering students regarded both onsite and online formats as providing useful skills that they intended to apply in practice. Follow-up evaluations suggest that the workshop leads to behavioural change, especially the use of proactive teamwork strategies intended to reduce the impact of unconscious bias. (shrink)

It has recently been argued that to tackle social injustice, implicit biases and unjust social structures should be targeted equally because they sustain and ontologically overlap with each other. Here I develop this thought further by relating it to the hypothesis of extended cognition. I argue that if we accept common conditions for extended cognition then people’s implicit biases are often partly realized by and so extended into unjust social structures. This supports the view that we should counteract psychological and (...) social contributors to injustice equally. But it also has a significant downside. If unjust social structures are part of people’s minds then dismantling these structures becomes more difficult than it currently is, as this will then require us to overcome widely accepted ethical and legal barriers protecting people’s bodily and personal integrity. Thus, while there are good grounds to believe that people’s biases and unjust social structures ontologically overlap, there are also strong ethical reasons to reject this view. Metaphysical and ethical intuitions about implicit bias hence collide in an important way. (shrink)

This paper conducted a preliminary study of reviewing and exploring bias strategies using a framework of a different discipline: change management. The hypothesis here is: If the major problem of implicit bias strategies is that they do not translate into actual changes in behaviors, then it could be helpful to learn from studies that have contributed to successful change interventions such as reward management, social neuroscience, health behavioral change, and cognitive behavioral therapy. The result of this integrated approach is: current (...) bias strategies can be improved and new ones can be developed with insight from adjunct study fields in change management; it could be more sustainable to invest in a holistic and proactive bias strategy approach that targets the social environment, eliminating the very condition under which biases arise; and while implicit biases are automatic, future studies should invest more on strategies that empower people as “change agents” who can act proactively to regulate the very environment that gives rise to their biased thoughts and behaviors. (shrink)

Empathic concern is essential to our social lives because it motivates helping behavior. It has, however, well-known shortcomings such as its limitation in scope. Here, we highlight a further shortcoming of empathic concern: it contributes little to understanding the relevant features of complex social situations, and unaided by further cognitive inputs, likely fails to produce effective helping. We then elaborate on the conditions needed for an accurate assessment of others’ situations: the ability to pay attention and try to understand others (...) for their own sake. We explain that when combining these abilities with the valuing aspect of empathic concern, we obtain “availability”, an understudied mental state which plays a crucial role in helping motivation. We provide a detailed definition of that notion and show how availability can be trained and exercised by health professionals in order to improve their care and relationships with patients. (shrink)

This paper focuses on the potential of “equitech”—AI technology that improves equity. Recently, interventions have been developed to reduce the harm of implicit bias, the automatic form of stereotype or prejudice that contributes to injustice. However, these interventions—some of which are assisted by AI-related technology—have significant limitations, including unintended negative consequences and general inefficacy. To overcome these limitations, we propose a two-dimensional framework to assess current AI-assisted interventions and explore promising new ones. We begin by using the case of human (...) resource recruitment as a focal point to show that existing approaches have exploited only a subset of the available solution space. We then demonstrate how our framework facilitates the discovery of new approaches. The first dimension of this framework helps us systematically consider the analytic information, intervention implementation, and modes of human-machine interaction made available by advancements in AI-related technology. The second dimension enables the identification and incorporation of insights from recent research on implicit bias intervention. We argue that a design strategy that combines complementary interventions can further enhance the effectiveness of interventions by targeting the various interacting cognitive systems that underlie implicit bias. We end with a discussion of how our cognitive interventions framework can have positive downstream effects for structural problems. (shrink)

BackgroundImplicit prejudice can lead to disparities in treatment. The effects of specialty and experience on implicit obesity and mental illness prejudice had not been explored. The main objective was to examine how specializing in psychiatry/general medicine and years of experience moderated implicit obesity and mental illness prejudice among Swiss physicians. Secondary outcomes included examining the malleability of implicit bias via two video interventions and a condition of cognitive load, correlations of implicit bias with responses to a clinical vignette, and correlations (...) with explicit prejudice. MethodsIn stage 1, participants completed an online questionnaire including a clinical vignette. In stage 2, implicit prejudice pre- and post- intervention was tested using a 4 × 4 between-subject design including a control group. In stage 3, explicit prejudice was tested with feeling thermometers and participants were debriefed. Participants were 133 psychiatrists and internists working in Geneva, hospital-based and private practice. Implicit prejudice was assessed using a Weight IAT and a Mental Illness IAT. Explicit feelings towards the obese and the mentally ill were measured using Feeling Thermometers. A clinical vignette assessed the level of concern felt for a fictional patient under four conditions: control, obese, depression, obese and depression. Linear regression was conducted to test for association of gender, experience, and specialty with responses to vignettes, pre-intervention IATs and explicit attitudes, and to test for association of interventions with post-intervention IATs and explicit attitudes. Reported effect sizes were computed using Cohen’s d. Two-tailed p < 0.05 was selected as the significance threshold.ResultsCompared to internists, psychiatrists showed significantly less implicit bias against mentally vs. physically ill people than internists and warmer explicit feelings towards the mentally ill. More experienced physicians displayed warmer explicit feelings towards the mentally ill and a greater level of concern for the fictional patients in the vignette than the less experienced, except when the patient was described as obese.ConclusionsSpecialty moderates both implicit and explicit mental illness prejudice. Experience moderates explicit mental illness bias and concern for patients. The effect of specialty on implicit prejudice seems to be based principally on self-selection. (shrink)

Stereotypes of girls having weaker mathematical abilities than boys are one factor reducing women’s representation in mathematics. Teachers, as powerful socializers, often hold math-gender stereotypes. Reducing math-gender stereotypes in teachers thus may foster women’s representation in mathematics. Yet knowing the stereotypes’ underlying assumptions is crucial to reducing it. Do math-gender stereotypes reflect elaborate, disproven theories about gender differences in math, meaning math-gender misconceptions? And if so, which math-gender misconceptions are behind math-gender stereotypes? This is the focus of the present research. (...) The relevant literature implies the existence of three distinct misconceptions: empathizing-systemizing, girls’ compensation, and girls’ non-compensability. We assessed these misconceptions in a student teacher sample using our newly developed Math-Gender Misconceptions Questionnaire. Our results offer support for the expected three-factor structure of math-gender misconceptions. All three math-gender misconceptions showed good to acceptable scale reliabilities. On average, preservice teachers did not hold math-gender misconceptions. But a subgroup of 48.2% of preservice teachers held at least one of the three misconceptions. The empathizing-systemizing misconception was the most prevalent among the three misconceptions. Descriptively, endorsing the math-gender stereotype correlated most strongly with the empathizing-systemizing and the girls’ compensation misconception. This may indicate that especially these two misconceptions partly underlie math-gender stereotypes. As a consequence, refutation instructions designed to reduce these misconceptions may be a promising method to weaken math-gender stereotypes. Further research is needed to investigate to what degree reducing the present misconceptions is related to reducing math-gender stereotypes. Hence, this study is the first one of a planned series of studies on the relation between math-gender misconceptions and math-gender stereotypes. (shrink)

The project of naturalizing ethics has multiple contributions, from cognitive and moral psychology to primatology, neuroscience or evolutionary theory. One of the strategies for naturalizing ethics has been to argue that moral norms and values can be explained away if we focus on their causal history, if it is possible to offer both an ultimate and proximate causal explanation for them. In this article, I will focus on the contribution of cognitive and moral psychology as a way of offering a (...) proximate causal explanation for moral judgments. I am mostly interested in understanding to what extent these cognitive and psychological questions have some bearing in the fields of ethics and meta-ethics. Does this research programme put at stake the contention that ethics is a manifestation of human rationality? Is it true that finding the cognitive underpinnings of some of our moral judgments vindicates some meta-ethical position, namely some kind of reductionist naturalism? In the end, I will argue that even if scientific disciplines such as cognitive psychology give us a naturalized picture of the moral agent, there seem to be no reasons to think that from a naturalized perspective of the agent capable of perceiving value it must follow the naturalization of value itself. (shrink)

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