Research suggests that interventions involving extensive training or counterconditioning can reduce implicit prejudice and stereotyping, and even susceptibility to stereotype threat. This research is widely cited as providing an “existence proof” that certain entrenched social attitudes are capable of change, but is summarily dismissed—by philosophers, psychologists, and activists alike—as lacking direct, practical import for the broader struggle against prejudice, discrimination, and inequality. Criticisms of these “debiasing” procedures fall into three categories: concerns about empirical efficacy, about practical feasibility, and about the (...) failure to appreciate the underlying structural-institutional nature of discrimination. I reply to these criticisms of debiasing, and argue that a comprehensive strategy for combating prejudice and discrimination should include a central role for training our biases away. (shrink)

A challenge we face in a world that has been shaped by, and continues to be shaped by, racist attitudes and institutions is that the evidence is often stacked in favor of racist beliefs. As a result, we may find ourselves facing the following conflict: what if the evidence we have supports something we morally shouldn’t believe? For example, it is morally wrong to assume, solely on the basis of someone’s skin color, that they’re a staff member. But, what if (...) you’re in a context where, because of historical patterns of discrimination, someone’s skin color is a very good indicator that they’re a staff member? When this sort of normative conflict looms, a conflict between moral considerations on the one hand and what you epistemically ought to believe given the evidence on the other, what should we do? It might be unfair to assume that they’re a staff member, but to ignore the evidence would mean risking inaccurate beliefs. Some, notably Tamar Gendler (2011), have suggested that we simply face a tragic irresolvable dilemma. In this chapter, I consider how these cases of conflict arise and I canvass the viability of suggested resolutions of the conflict. In the end, I argue that there’s actually no conflict here. Moral considerations can change how we epistemically should respond to the evidence. (shrink)

What can we do—and what should we do—to fight against bias? This final chapter introduces empirically-tested interventions for combating implicit (and explicit) bias and promoting a fairer world, from small daily-life debiasing tricks to larger structural interventions. Along the way, this chapter raises a range of moral, political, and strategic questions about these interventions. This chapter further stresses the importance of admitting that we don’t have all the answers. We should be humble about how much we still don’t know and (...) dedicate efforts to gathering as much knowledge as possible. Even so, we know enough now to start making a difference, and this chapter ultimately aims to chip away at the gap between our abstract commitments to treat people fairly and our lived habits and experiences, which continue to be shaped by implicit and explicit prejudices and stereotypes about race, gender, and other social categories. (shrink)

More than forty years have passed since Congress, in response to the Civil Rights Movement, enacted sweeping antidiscrimination laws in the Civil Rights Act of 1964, the Voting Rights Act of 1965, and the Fair Housing Act of 1968. As a signal achievement of that legacy, in 2008, Americans elected their first African American president. Some would argue that we have finally arrived at a postracial America, butThe Imperative of Integration indicates otherwise. Elizabeth Anderson demonstrates that, despite progress toward racial (...) equality, African Americans remain disadvantaged on virtually all measures of well-being. Segregation remains a key cause of these problems, and Anderson skillfully shows why racial integration is needed to address these issues. Weaving together extensive social science findings--in economics, sociology, and psychology--with political theory, this book provides a compelling argument for reviving the ideal of racial integration to overcome injustice and inequality, and to build a better democracy. -/- Considering the effects of segregation and integration across multiple social arenas, Anderson exposes the deficiencies of racial views on both the right and the left. She reveals the limitations of conservative explanations for black disadvantage in terms of cultural pathology within the black community and explains why color blindness is morally misguided. Multicultural celebrations of group differences are also not enough to solve our racial problems. Anderson provides a distinctive rationale for affirmative action as a tool for promoting integration, and explores how integration can be practiced beyond affirmative action. -/- Offering an expansive model for practicing political philosophy in close collaboration with the social sciences, this book is a trenchant examination of how racial integration can lead to a more robust and responsive democracy. (shrink)

In recruitment, promotion, admission, and other forms of wealth and power apportion, an evaluator typically ranks a set of candidates in terms of their competence. If the evaluator is prejudiced, the resulting ranking will misrepresent the candidates’ actual rankings. This constitutes not only a moral and a practical problem, but also an epistemological one, which begs the question of what we should do—epistemologically—to mitigate it. In a recent paper, Jönsson and Sjödahl in [Episteme 14:499–517, 2017], argue that the epistemic problem (...) can be fruitfully addressed by way of a novel statistical method that changes the products of biased behaviour, i.e. the rankings themselves, rather than the biased persons. Jönsson and Sjödahl’s pioneering proposal is a both a welcome addition to the literature on implicit bias, due the problems with existing implicit bias interventions [see e.g. Lai et al. in J Exp Psychol Gen 143:1765–1785; J Exp Psychol Gen 145:1001–1016, 2014; 2016; Forscher et al. in J Person Soc Psychol 117:522–559, 2019] but also to the literature on prejudice more generally, where many proposed prejudice-reduction strategies enjoy less than adequate empirical support [Paluck and Green in Ann Rev Psychol 60:339–367, 2009]. Their proposal, however, needs supplementation in two ways: the circumstances that must hold in order for it to work needs to be refined, and their claim that it works as intended in these circumstances needs to be validated. We argue that four of Jönsson and Sjödahl’s method’s presumed presuppositions can be weakened, but needs to be supplemented by two additional assumptions, overlooked by Jönsson and Sjödahl. Moreover, we demonstrate that the method does work as intended by way of a statistical simulation. (shrink)

In spite of our good intentions and explicit egalitarian convictions, we habitually disfavor the underprivileged. The rapidly growing literature on implicit bias – unconscious, automatic tendencies to associate negative traits with members of particular social groups – points towards explanations of this dissonance, although rarely towards generalizable solutions. In a recent paper, Jennifer Saul draws attention to the alarming epistemological problems that implicit bias carries with it; since our judgments about each other are likely influenced by implicit bias, we have (...) good reason to doubt their veracity. In this paper we explore a novel way to come to terms with the epistemological problem as it manifests itself in ranking situations, i.e. how we can know that the way in which we have ranked a group of people for a certain position, reflects their actual competence. On our approach, rather than attempting to make people less biased, we suggest that biased behavior can sometimes be corrected after the fact. In particular, the veracity of rankings can sometimes be improved by modifying the rankings directly. We investigate three methods that modify biased rankings, and argue that the last of these solves the epistemological problem that we are concerned with. (shrink)

An implicit association test (IAT) measures differential association of 2 target concepts with an attribute. The 2 concepts appear in a 2-choice task (e.g., flower vs. insect names), and the attribute in a 2nd task (e.g., pleasant vs. unpleasant words for an evaluation attribute). When instructions oblige highly associated categories (e.g., flower + pleasant) to share a response key, performance is faster than when less associated categories (e.g., insect + pleasant) share a key. This performance difference implicitly measures differential association (...) of the 2 concepts with the attribute. In 3 experiments, the IAT was sensitive to (a) near-universal evaluative differences (e.g., flower vs. insect), (b) expected individual differences in evaluative associations (Japanese + pleasant vs. Korean + pleasant for Japanese vs. Korean subjects), and (c) consciously disavowed evaluative differences (Black + pleasant vs. White + pleasant for self-described unprejudiced White subjects). (PsycINFO Database Record (c) 2016 APA, all rights reserved). (shrink)