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

It is generally agreed that one origin of machine bias is resulting from characteristics within the dataset on which the algorithms are trained, i.e., the data does not warrant a generalized inference. We, however, hypothesize that a different ‘mechanism’ may also be responsible for machine bias, namely that biases may originate from the programmers’ cultural background, including education or line of work, or the contextual programming environment, including software requirements or developer tools. Combining an experimental and comparative design, (...) we study the effects of cultural and contextual metaphors, and test whether each of these are ‘transferred’ from the programmer to the program, thus constituting a machine bias. Our results show that cultural metaphors influence the programmer’s choices and contextual metaphors induced through priming can be used to moderate or exacerbate the effects of the cultural metaphors. Our studies are purposely performed with users of varying educational backgrounds and programming skills stretching from novice to proficient. (shrink)

In many critical investigations of machine vision, the focus lies almost exclusively on dataset bias and on fixing datasets by introducing more and more diverse sets of images. We propose that machine vision systems are inherently biased not only because they rely on biased datasets but also because theirperceptual topology, their specific way of representing the visual world, gives rise to a new class of bias that we callperceptual bias. Concretely, we define perceptual topology as the set (...) of those inductive biases in machine vision systems that determine its capability to represent the visual world. Perceptual bias, then, describes the difference between the assumed “ways of seeing” of a machine vision system, our reasonable expectations regarding its way of representing the visual world, and its actual perceptual topology. We show how perceptual bias affects the interpretability of machine vision systems in particular, by means of a close reading of a visualization technique called “feature visualization”. We conclude that dataset bias and perceptual bias both need to be considered in the critical analysis of machine vision systems and propose to understand critical machine vision as an important transdisciplinary challenge, situated at the interface of computer science and visual studies/Bildwissenschaft. (shrink)

The bias–variance tradeoff is a theoretical concept that suggests machine learning algorithms are susceptible to two kinds of error, with some algorithms tending to suffer from one more than the other. In this letter, we claim that the bias–variance tradeoff is a general concept that can be applied to human cognition as well, and we discuss implications for research in cognitive science. In particular, we show how various strands of research in cognitive science can be interpreted in (...) light of the bias–variance tradeoff, giving insight into individual differences in learning, the nature of cognitive processes, and debates in cognitive science research. (shrink)

In this paper, I sketch a general framework for theorizing about bias and bias attributions. According to the account, paradigmatic cases of bias involve systematic departures from genuine norms. I attempt to show that the account illuminates a number of important psychological phenomena, including: the fact that accusations of bias frequently inspire not only denials but also countercharges of bias (“you only think that I'm biased because you're biased!”); the fact that we tend to see (...) ourselves as less biased than our peers (the so‐called ‘bias blind spot’); and the fact that we tend to see people who share our views as less biased than people who don't. I explore the circumstances in which we're committed to believing that those who disagree with us are not only mistaken but also biased simply because they disagree with us in the way that they do. In addition, the account also sheds light on another notorious and well‐documented psychological phenomenon: the fact that introspection is an unreliable way of detecting our biases. On the account that I offer, the unreliability of introspection for this purpose isn't a contingent fact that depends on the finer details of human psychology but rather holds of necessity: even God could not have made us creatures who reliably detect our own biases by way of introspection. (shrink)

The spatial composition of human portraits obeys historically changing cultural norms. We show that it is also affected by cognitive factors that cause greater spontaneous attention to what is in front rather in the back of an agent. Scenes with more space in front of a directed object are both more often produced and judged as more aesthetically pleasant. This leads to the prediction that, in profile‐oriented human portraits, compositions with more space in front of depicted agents (a (...) “forward bias”) should be over‐represented. By analyzing a large dataset (total N of 1,831 paintings by 582 unique identified European painters from the 15th to the 20th century), we found evidence of this forward bias: Painters tended to put more free space in front of, rather than behind, the sitters. Additionally, we found evidence that this forward bias became stronger when cultural norms of spatial composition favoring centering became less stringent. (shrink)

For synergistic interactions between humans and artificial intelligence (AI) systems, AI outputs often need to be explainable to people. Explainable AI (XAI) systems are commonly tested in human user studies. However, whether XAI researchers consider potential cultural differences in human explanatory needs remains unexplored. We highlight psychological research that found significant differences in human explanations between many people from Western, commonly individualist countries and people from non-Western, often collectivist countries. We argue that XAI research currently overlooks these (...) variations and that many popular XAI designs implicitly and problematically assume that Western explanatory needs are shared cross-culturally. Additionally, we systematically reviewed over 200 XAI user studies and found that most studies did not consider relevant cultural variations, sampled only Western populations, but drew conclusions about human-XAI interactions more generally. We also analyzed over 30 literature reviews of XAI studies. Most reviews did not mention cultural differences in explanatory needs or flag overly broad cross-cultural extrapolations of XAI user study results. Combined, our analyses provide evidence of a cultural bias toward Western populations in XAI research, highlighting an important knowledge gap regarding how culturally diverse users may respond to widely used XAI systems that future work can and should address. (shrink)

Are human beings irredeemably irrational? If so, why? In this article, I suggest that we need a broader appreciation of thought and reasoning to understand why people get things wrong. Although we can never escape cognitive bias, learning to recognize and understand it can help us push back against its dangers – and in particular to do so collectively and collaboratively.

Across the world, artificial intelligence (AI) technologies are being more widely employed in public sector decision-making and processes as a supposedly neutral and an efficient method for optimizing delivery of services. However, the deployment of these technologies has also prompted investigation into the potentially unanticipated consequences of their introduction, to both positive and negative ends. This paper chooses to focus specifically on the relationship between gender bias and AI, exploring claims of the neutrality of such technologies and how its (...) understanding of bias could influence policy and outcomes. Building on a rich seam of literature from both technological and sociological fields, this article constructs an original framework through which to analyse both the perpetuation and mitigation of gender biases, choosing to categorize AI technologies based on whether their input is text or images. Through the close analysis and pairing of four case studies, the paper thus unites two often disparate approaches to the investigation of bias in technology, revealing the large and varied potential for AI to echo and even amplify existing human bias, while acknowledging the important role AI itself can play in reducing or reversing these effects. The conclusion calls for further collaboration between scholars from the worlds of technology, gender studies and public policy in fully exploring algorithmic accountability as well as in accurately and transparently exploring the potential consequences of the introduction of AI technologies. (shrink)

Two humanist, critical approaches—those of Dorothy Dinnerstein and Immanuel Kant—are summarized, compared, and employed to critique gender bias in science education. The value of Dinnerstein’s approach lies in her way of seeing conventional “masculinity” and conventional “femininity” as developing in relation to each other from early childhood. Because of women’s dominance of early childcare and adults’ enduring, sexist resentment of that dominance, women become inhumanely associated with the non-adult qualities of immaturity, dependence, and childish vulnerability and punish-ability; and male (...) human beings—to whom woman-resenting convention assigns the impossible task of absolutely triumphing over “the feminine,” childhood experience, and all human vulnerability—become inhumanely held to unachievable standards of super-hero invulnerability and god-like mental and practical infallibility. The value of Kant’s approach lies in his insistence that our sense of what is right and necessary for social progress must arise in a practically engaged and experientially full manner, rather than (1) from concepts (such as rigid ideological prescriptions) conceived as being detached from sense experience and as arising from an otherworldly, divine or quasi-divine realm of moral infallibility (such concepts being conventionally associated with “masculine” authority and leadership), or (2) from a sense of being trapped in what—in a given historical, cultural, or experiential moment—may appear as an absolute and unchangeable reality of embodied human experience (an anti-critical, anti-intellectual, and anti-progressive sense of things often associated with disempowered “feminine” experience). I demonstrate that critique integrating these approaches is useful in a science education setting. (shrink)

To Vallortigara & Rogers's (V&R's) evidence of everyday directional asymmetries in the natural environment of a variety of species, we offer one more example for human beings. It is the bias for holding an infant on the left side, and it illustrates several themes in the target article.

Over the years, companies have adopted hiring algorithms because they promise wider job candidate pools, lower recruitment costs and less human bias. Despite these promises, they also bring perils. Using them can inflict unintentional harms on individual human rights. These include the five human rights to work, equality and nondiscrimination, privacy, free expression and free association. Despite the human rights harms of hiring algorithms, the AI ethics literature has predominantly focused on abstract ethical principles. This (...) is problematic for two reasons. First, AI principles have been criticized for being vague and not actionable. Second, the use of vague ethical principles to discuss algorithmic risks does not provide any accountability. This lack of accountability creates an algorithmic accountability gap. Closing this gap is crucial because, without accountability, the use of hiring algorithms can lead to discrimination and unequal access to employment opportunities. This paper makes two contributions to the AI ethics literature. First, it frames the ethical risks of hiring algorithms using international human rights law as a universal standard for determining algorithmic accountability. Second, it evaluates four types of algorithmic impact assessments in terms of how effectively they address the five human rights of job applicants implicated in hiring algorithms. It determines which of the assessments can help companies audit their hiring algorithms and close the algorithmic accountability gap. (shrink)

Current research in artificial intelligence (AI) sheds light on algorithmic bias embedded in AI systems. The underrepresentation of women in the AI design sector of the tech industry, as well as in training datasets, results in technological products that encode gender bias, reinforce stereotypes and reproduce normative notions of gender and femininity. Biased behaviour is notably reflected in anthropomorphic AI systems, such as personal intelligent assistants (PIAs) and chatbots, that are usually feminized through various design parameters, such as (...) names, voices and traits. Gendering of AI entities, however, is often reduced to the encoding of stereotypical behavioural patterns that perpetuate normative assumptions about the role of women in society. The impact of this behaviour on social life increases, as human-to-(anthropomorphic)machine interactions are mirrored in human-to-human social interactions. This article presents current critical research on AI bias, focusing on anthropomorphic systems. Moreover, it discusses the significance of women’s engagement in AI design and programming, by presenting selected case studies of contemporary female media artists and designers. Finally, it suggests that women, through their creative practice, provide feminist and critical approaches to AI design which are essential for imagining alternative, inclusive, ethic and de-biased futures for anthropomorphic AIs. (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)

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

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)

Some recent work in philosophy of religion addresses what can be called the “axiological question,” i.e., regardless of whether God exists, would it be good or bad if God exists? Would the existence of God make the world a better or a worse place? Call the view that the existence of God would make the world a better place “Pro-Theism.” We argue that Pro-Theism is not implausible, and moreover, many Theists, at least, (often implicitly) think that it is true. That (...) is, many Theists think that various good outcomes would arise if Theism is true. We then discuss work in cognitive science concerning human cognitive bias, before discussing two noteworthy attempts to show that at least some religious beliefs arise because of cognitive bias: Hume’s, and Draper’s and Nichols’s. We then argue that, as a result of certain cognitive biases that result when good outcomes might be at stake, Pro-Theism causes many Theists to inflate the epistemic probability that God exists, and as a result, Theists should lower the probability they assign to God’s existence. Finally, based our arguments, we develop a novel objection to Pascal’s wager. (shrink)

In this paper I take issue with Heidegger's use of the concept of death as a means of disclosing human finitude. I argue that Being‐towards‐death is inadequate to the disclosure of Dasein's thrownness which is necessary for the kind of authentic historizing that Heidegger describes and furthermore leads to a reading of authenticity which is preclusive of Being‐with‐Others, I suggest that this difficulty may be alleviated through increased attention to the opposite boundary of Dasein's existence, namely its birth. Although (...) I do not pursue the project here of conducting a phenomenology of birth, I suggest some directions for proceeding with that task, and I illustrate that a greater emphasis on Dasein's beginning will increase the richness of our understanding of our Being‐with‐Others. (shrink)

This chapter focuses on one of the common fallacies in Western philosophy, “confirmation bias”. Confirmation bias is the human tendency only to look for evidence that confirms what one wants to believe or what one already thinks is true. Usually people are not too keen to look for evidence against what they want to believe is true. The human propensity for self‐delusion is strong. When one is confronted with sufficient evidence against some belief that one holds, (...) what one should do is reject that belief ‐ at least if one is concerned with truth. When we surround ourselves with people and views that already agree with our own, we can draw erroneous conclusions. Confirmation bias greatly hinders one's ability to find the truth. Confirmation bias and evidence denial is why we start “seeing something everywhere” once you learn about it. The more scrutiny a belief can withstand, the more likely it is that it's true. (shrink)

Confirmation bias has been widely studied for its role in failures of reasoning. Individuals exhibiting confirmation bias fail to engage with information that contradicts their current beliefs, and, as a result, can fail to abandon inaccurate beliefs. But although most investigations of confirmation bias focus on individual learning, human knowledge is typically developed within a social structure. We use network models to show that moderate confirmation bias often improves group learning. However, a downside is that (...) a stronger form of confirmation bias can hurt the knowledge producing capacity of the community. (shrink)

Does algorithmic political bias contribute to an entrenchment and polarization of political positions? Franke argues that it may do so because the bias involves classifications of people as liberals, conservatives, etc., and individuals often conform to the ways in which they are classified. I provide a novel example of this phenomenon in human–computer interactions and introduce a social psychological mechanism that has been overlooked in this context but should be experimentally explored. Furthermore, while Franke proposes that algorithmic (...) political classifications entrench political identities, I contend that they may often produce the opposite result. They can lead people to change in ways that disconfirm the classifications. Consequently and counterintuitively, algorithmic political bias can in fact decrease political entrenchment and polarization. (shrink)

How should we handle ethical issues related to emerging science and technology in a rational way? This is a crucial issue in our time. On the one hand, there is great optimism with respect to technology. On the other, there is pessimism. As both perspectives are based on scarce evidence, they may appear speculative and irrational. Against the pessimistic perspective to emerging technology, it has been forcefully argued that there is a status quo bias (SQB) fuelling irrational attitudes to (...) emergent science and technology and greatly hampering useful development and implementation. Therefore, this article starts by analysing the SQB using human enhancement as a case study. It reveals that SQB may not be as prominent in restricting the implementation of emergent technologies as claimed in the ethics literature, because SQB (a) is fuelled by other and weaker drivers than those addressed in the literature, (b) is at best one amongst many drivers of attitudes towards emergent science and technology, and (c) may not be a particularly prominent driver of irrational decision‐making. While recognizing that SQB can be one driver behind pessimism, this article investigates other and counteracting forces that may be as strong as SQB. Progress bias is suggested as a generic term for the various drivers of unwarranted science and technology optimism. Based on this analysis, a test for avoiding or reducing this progress bias is proposed. Accordingly, we should recognize and avoid a broad range of biases in the assessment of emerging and existing science and technology in order to promote an open and transparent deliberation. (shrink)

이 글은 인공지능에 의한 차별과 그 책임 논의를 철학적 차원에서 본격적으로 연구하기에 앞선 예비적 고찰이다. 인공지능에 의한 차별을 철학자들의 연구를 요하는 당면 ‘문제’로 제기하고, 이를 위해 ‘인공지능에 의한 차별’이라는 문제의 성격과 원인을 규명하는 것이 이 글의 주된 목적이다. 인공지능은 기존 차별을 그대로 반복하여 현존하는 차별의 강화 및 영속화를 야기할 수 있으며, 이는 먼 미래의 일이 아니다. 이러한 문제는 현재 발생 중이며 공동체적 대응을 요구한다. 그러나 철학자의 입장에서 그와 관련한 책임 논의를 다루기는 쉽지 않다. 그 이유는 크게 인공지능의 복잡한 기술적 문제와 (...) 전통적 책임 개념의 한계라 할 수 있다. 여기서는 특히 인공지능과 관련된 기술적 개념을 중심으로 문제를 파악하고, 이와 관련하여 인간 행위자의 책임 및 그 역량을 규명한다. 통칭 인공지능으로 이해되는 것은 알고리즘에 기반한 자동화 시스템이며, 알고리즘의 편향성이 인공지능에 의한 차별을 야기할 수 있다. 알고리즘이 겉보기에 인간 행위자와 독립적으로 작동하는 것처럼 보일지라도, 인간 행위자가 그에 개입하고 있음을 밝힌다. 인간 행위자가 편향된 입력 자료를 사용하거나 편향된 설계를 할 수 있다는 점에서, 알고리즘의 편향성은 인간 행위자의 편향성과 불가분의 관계이기 때문이다. 그러므로 인간 행위자는 인공지능에 의한 차별문제에 대응할 책임이 있으며, 책임 역량을 갖고 있다. 마지막으로 인공지능에 의한 차별 논의를 위한 철학 연구의 추후 과제를 정리한다. (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)

Social isolation and loneliness are growing concerns around the globe that put people at increased risk of disease and early death. One much‐touted approach to addressing them is deploying artificially intelligent agents to serve as companions for socially isolated and lonely people. Focusing on digital humans, we consider evidence and ethical arguments for and against this approach. We set forth and defend public health policies that respond to concerns about replacing humans, establishing inferior relationships, algorithmic bias, distributive justice, and (...) data privacy. (shrink)

A cognitive bias is a pattern of deviation in our judgment or our processing of what we perceive. Its raison d'être is the evolutionary need to produce immediate judgments in order to adopt a position quickly in response to stimuli, problems, or situations that catch our attention for some reason. They have a social dimension because they are present in the interactions and decision-making processes of ordinary life. They can be understood to be an adaptive response to human (...) inability to process all of the available information that we receive and selectively filter through biased reasoning or through procedures and heuristic rules. (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)

The question of bias in methodology and terminology is a problem that faces researchers east, west, north and south; however, it faces Third World intellectuals with special keenness. For although they write in a cultural environment that has its own specific conceptual and cultural paradigms, they nevertheless encounter a foreign paradigm which attempts to impose itself upon their society and upon their very imagination and thoughts. When the term “developmental psychology” for instance is used in the West Arab scholars (...) also say “developmental psychology”, when “applied psychology” is mentioned they hurry to use the same term etc. Why not establish a new science with its own mechanisms, methodologies and points of reference to deal with epistemological biases and open up the gate of ijtihad with respect to them? This collection of papers aims to discover some of the biases latent in our terminology, methodologies, research tools, and conceptual principles, and to propose alternative ones marked by a greater degree of independence and neutrality. This is not to belittle the human value of the West’s creative contributions but to emphasize the danger of making it the ultimate point of reference and then trying to continuously play “catch up” with it. (shrink)

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

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

Ordinary cognition is subject to the influence of a variety of systematic distortions or biases. This chapter looks at the use of some collective cognition techniques to correct for individual cognitive bias. It introduces the possibility of group‐level corrections to cognitive bias and raises the problem of biases that emerge at the group level. The chapter discusses how to ameliorate some of the cognitive biases that affect individuals by utilizing group processes and choice architecture. Some examples of the (...) use of the internet to create corrections to cognitive biases are also discussed. Then the possibility of institutions engineering solutions to individual cognitive biases is considered. (shrink)

According to Russell and Norvig’s (2009) classification, Artificial Intelligence (AI) is the field that aims at building systems which either think rationally, act rationally, think like humans, or...

Written by a diverse range of scholars, this accessible introductory volume asks: What is implicit bias? How does implicit bias compromise our knowledge of others and social reality? How does implicit bias affect us, as individuals and participants in larger social and political institutions, and what can we do to combat biases? An interdisciplinary enterprise, the volume brings together the philosophical perspective of the humanities with the perspective of the social sciences to develop rich lines of inquiry. (...) Its 12 chapters are written in a non-technical style, using relatable examples that help readers understand what implicit bias is, its significance, and the controversies surrounding it. Each chapter includes discussion questions and additional annotated reading suggestions. And a companion webpage contains teaching resources. The volume is invaluable resource for students―and researchers―seeking to understand criticisms surrounding implicit bias, as well as how one might answer them by adopting a more nuanced understanding of bias and its role in maintaining social injustice. (shrink)

When Artificial Intelligence (AI) is applied in decision-making that affects people’s lives, it is now well established that the outcomes can be biased or discriminatory. The question of whether algorithms themselves can be among the sources of bias has been the subject of recent debate among Artificial Intelligence researchers, and scholars who study the social impact of technology. There has been a tendency to focus on examples, where the data set used to train the AI is biased, and denial (...) on the part of some researchers that algorithms can also be biased. Here we illustrate the point that algorithms themselves can be the source of bias with the example of collaborative filtering algorithms for recommendation and search. These algorithms are known to suffer from cold-start, popularity, and homogenizing biases, among others. While these are typically described as statistical biases rather than biases of moral import; in this paper we show that these statistical biases can lead directly to discriminatory outcomes. The intuitive idea is that data points on the margins of distributions of human data tend to correspond to marginalized people. The statistical biases described here have the effect of further marginalizing the already marginal. Biased algorithms for applications such as media recommendations can have significant impact on individuals’ and communities’ access to information and culturally-relevant resources. This source of bias warrants serious attention given the ubiquity of algorithmic decision-making. (shrink)

The Radical Behavioral Challenge (RBC) contends that due to normal human cognitive biases, many standard prescriptions of business ethics run afoul of the principle that ‘ought implies can.’ Von Kriegstein responds to this challenge by arguing that those prescriptions are wide-scope obligations that can be fulfilled by recusing oneself or by establishing appropriate safeguards. I argue that this solution falls short of fully resolving the RBC because individuals will often be incapable of recognizing when they are biased and incapable (...) of establishing appropriate safeguards. (shrink)

Written by a diverse range of scholars, this accessible introductory volume asks: What is implicit bias? How does implicit bias compromise our knowledge of others and social reality? How does implicit bias affect us, as individuals and participants in larger social and political institutions, and what can we do to combat biases? An interdisciplinary enterprise, the volume brings together the philosophical perspective of the humanities with the perspective of the social sciences to develop rich lines of inquiry. (...) It is written in a non-technical style, using relatable examples that help readers understand what implicit bias is, its significance, and the controversies surrounding it. Each chapter includes discussion questions and additional reading suggestions. A companion webpage contains teaching resources. The volume will be an invaluable resource for students—and researchers—seeking to understand criticisms surrounding implicit bias, as well as how one might answer them by adopting a more nuanced understanding of bias and its role in maintaining social injustice. (shrink)

Bias and prejudice are well known aspects of all societies and political arenas. They motivate a wide variety of fear-mongering policies and seem to be deeply ingrained in the hearts and minds of people, interfering with their reasoning and better judgement. In this paper, I explore how bias and prejudice come about and how they can be put to more productive use in a democratic context. Humans aren’t as rational as we might expect. We often fail to think (...) logically and applying abstract reasoning is a challenge. When concepts are difficult for us to grasp, we often use our pre-existing ideas and experiences. Our biases and pre-conceived notions allow us to make better sense of complex problems. Bias is unavoidable. If we accept that we have pre-existing beliefs and can identify them accurately, they can serve an epistemic purpose in deliberation. We can use bias and prejudice as the starting point for meaningful inquiry as well as critical self-reflection. In order to accomplish this we need to place a greater educational focus on critical thinking skills. This type of education develops both our specific skills for analysis, evaluation and problem solving, but also builds the essential dispositions for open-mindedness and tolerance that are essential for effective deliberation. (shrink)