In the age of Big Data, companies and governments are increasingly using algorithms to inform hiring decisions, employee management, policing, credit scoring, insurance pricing, and many more aspects of our lives. Artificial intelligence systems can help us make evidence-driven, efficient decisions, but can also confront us with unjustified, discriminatory decisions wrongly assumed to be accurate because they are made automatically and quantitatively. It is becoming evident that these technological developments are consequential to people’s fundamental human rights. Despite increasing attention to (...) these urgent challenges in recent years, technical solutions to these complex socio-ethical problems are often developed without empirical study of societal context and the critical input of societal stakeholders who are impacted by the technology. On the other hand, calls for more ethically and socially aware AI often fail to provide answers for how to proceed beyond stressing the importance of transparency, explainability, and fairness. Bridging these socio-technical gaps and the deep divide between abstract value language and design requirements is essential to facilitate nuanced, context-dependent design choices that will support moral and social values. In this paper, we bridge this divide through the framework of Design for Values, drawing on methodologies of Value Sensitive Design and Participatory Design to present a roadmap for proactively engaging societal stakeholders to translate fundamental human rights into context-dependent design requirements through a structured, inclusive, and transparent process. (shrink)

The goal of this paper is to articulate and advocate for an enhanced role for philosophers of science in the domain of science policy as well as within the science curriculum. I argue that philosophy of science as a field can learn from the successes as well as the mistakes of bioethics and begin to develop a new model that includes robust contributions to the science classroom, research collaborations with scientists, and a role for public philosophy through involvement in science (...) policy development. Through an analysis of two case studies, I illustrate how philosophers of science can make effective and productive contributions to science education as well as to interdisciplinary scientific research, and argue for the essential role of philosophers of science in the realm of science policy. (shrink)

Most discussions of racial fetish center on the question of whether it is caused by negative racial stereotypes. In this paper I adopt a different strategy, one that begins with the experiences of those targeted by racial fetish rather than those who possess it; that is, I shift focus away from the origins of racial fetishes to their effects as a social phenomenon in a racially stratified world. I examine the case of preferences for Asian women, also known as ‘yellow (...) fever’, to argue against the claim that racial fetishes are unobjectionable if they are merely based on personal or aesthetic preference rather than racial stereotypes. I contend that even if this were so, yellow fever would still be morally objectionable because of the disproportionate psychological burdens it places on Asian and Asian-American women, along with the role it plays in a pernicious system of racial social meanings. (shrink)

Although decision‐making algorithms are not new to medicine, the availability of vast stores of medical data, gains in computing power, and breakthroughs in machine learning are accelerating the pace of their development, expanding the range of questions they can address, and increasing their predictive power. In many cases, however, the most powerful machine learning techniques purchase diagnostic or predictive accuracy at the expense of our ability to access “the knowledge within the machine.” Without an explanation in terms of reasons or (...) a rationale for particular decisions in individual cases, some commentators regard ceding medical decision‐making to black box systems as contravening the profound moral responsibilities of clinicians. I argue, however, that opaque decisions are more common in medicine than critics realize. Moreover, as Aristotle noted over two millennia ago, when our knowledge of causal systems is incomplete and precarious—as it often is in medicine—the ability to explain how results are produced can be less important than the ability to produce such results and empirically verify their accuracy. (shrink)

In analyzing oppressive systems like racism, social theorists have articulated accounts of the dynamic interaction and mutual dependence between psychological components, such as individuals’ patterns of thought and action, and social components, such as formal institutions and informal interactions. We argue for the further inclusion of physical components, such as material artifacts and spatial environments. Drawing on socially situated and ecologically embedded approaches in the cognitive sciences, we argue that physical components of racism are not only shaped by, but also (...) shape psychological and social components of racism. Indeed, while our initial focus is on racism and racist things, we contend that our framework is also applicable to other oppressive systems, including sexism, classism, and ableism. This is because racist things are part of a broader class of oppressive things, which are material artifacts and spatial environments that are in congruence with an oppressive system. (shrink)

The usefulness of machine learning algorithms has led to their widespread adoption prior to the development of a conceptual framework for making sense of them. One common response to this situation is to say that machine learning suffers from a “black box problem.” That is, machine learning algorithms are “opaque” to human users, failing to be “interpretable” or “explicable” in terms that would render categorization procedures “understandable.” The purpose of this paper is to challenge the widespread agreement about the existence (...) and importance of a black box problem. The first section argues that “interpretability” and cognates lack precise meanings when applied to algorithms. This makes the concepts difficult to use when trying to solve the problems that have motivated the call for interpretability. Furthermore, since there is no adequate account of the concepts themselves, it is not possible to assess whether particular technical features supply formal definitions of those concepts. The second section argues that there are ways of being a responsible user of these algorithms that do not require interpretability. In many cases in which a black box problem is cited, interpretability is a means to a further end such as justification or non-discrimination. Since addressing these problems need not involve something that looks like an “interpretation” of an algorithm, the focus on interpretability artificially constrains the solution space by characterizing one possible solution as the problem itself. Where possible, discussion should be reformulated in terms of the ends of interpretability. (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)

I use a theorem from machine learning, called the “No Free Lunch” theorem to support the claim that non-epistemic values are essential to theory choice. I argue that NFL entails that predictive accuracy is insufficient to favor a given theory over others, and that NFL challenges our ability to give a purely epistemic justification for using other traditional epistemic virtues in theory choice. In addition, I argue that the natural way to overcome NFL’s challenge is to use non-epistemic values. If (...) my argument holds, non-epistemic values are entangled in theory choice regardless of human limitations and regardless of the subject matter. Thereby, my argument overcomes objections to the main lines of argument revealing the role of values in theory choice. At the end of the paper, I argue that, contrary to common conception, the epistemic challenge arising from NFL is distinct from Hume’s problem of induction and other forms of underdetermination. (shrink)

Scientists depend on complex computational systems that are often ineliminably opaque, to the detriment of our ability to give scientific explanations and detect artifacts. Some philosophers have s...

Data-driven, decision-making technologies used in the justice system to inform decisions about bail, parole, and prison sentencing are biased against historically marginalized groups (Angwin, Larson, Mattu, & Kirchner 2016). But these technologies’ judgments—which reproduce patterns of wrongful discrimination embedded in the historical datasets that they are trained on—are well-evidenced. This presents a puzzle: how can we account for the wrong these judgments engender without also indicting morally permissible statistical inferences about persons? I motivate this puzzle and attempt an answer.

If someone says, “Asians are good at math” or “women are empathetic,” I might interject, “you're stereotyping” in order to convey my disapproval of their utterance. But why is stereotyping wrong? Before we can answer this question, we must better understand what stereotypes are and what stereotyping is. In this essay, I develop what I call the descriptive view of stereotypes and stereotyping. This view is assumed in much of the psychological and philosophical literature on implicit bias and stereotyping, yet (...) it has not been sufficiently defended. The main objection to the descriptive view is that it fails to include the common-sense idea that stereotyping is always objectionable. I argue that this is actually a benefit of the view. In the essay's final part, I put forward two hypotheses that would validate the claim that stereotyping is always morally or epistemically wrong. If these hypotheses are false—which is very likely—we have little reason to build moral or epistemic defect into the very idea of a stereotype. Moreover, we must abandon the seemingly attractive claim that judging individuals based on group membership is intrinsically wrong. (shrink)

Typically, fair machine learning research focuses on a single decision maker and assumes that the underlying population is stationary. However, many of the critical domains motivating this work are characterized by competitive marketplaces with many decision makers. Realistically, we might expect only a subset of them to adopt any non-compulsory fairness-conscious policy, a situation that political philosophers call partial compliance. This possibility raises important questions: how does partial compliance and the consequent strategic behavior of decision subjects affect the allocation outcomes? (...) If k% of employers were to voluntarily adopt a fairness-promoting intervention, should we expect k% progress (in aggregate) towards the benefits of universal adoption, or will the dynamics of partial compliance wash out the hoped-for benefits? How might adopting a global (versus local) perspective impact the conclusions of an auditor? In this paper, we propose a simple model of an employment market, leveraging simulation as a tool to explore the impact of both interaction effects and incentive effects on outcomes and auditing metrics. Our key findings are that at equilibrium: (1) partial compliance by k% of employers can result in far less than proportional (k%) progress towards the full compliance outcomes; (2) the gap is more severe when fair employers match global (vs local) statistics; (3) choices of local vs global statistics can paint dramatically different pictures of the performance vis-a-vis fairness desiderata of compliant versus non-compliant employers; (4) partial compliance based on local parity measures can induce extreme segregation. Finally, we discuss implications for auditors and insights concerning the design of regulatory frameworks. (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)

Very diverse societies pose real problems for Rawlsian models of public reason. This is for two reasons: first, public reason is unable accommodate diverse perspectives in determining a regulative ideal. Second, regulative ideals are unable to respond to social change. While models based on public reason focus on the justification of principles, this book suggests that we need to orient our normative theories more toward discovery and experimentation. The book develops a unique approach to social contract theory that focuses on (...) diverse perspectives. It offers a new moral stance that author Ryan Muldoon calls, "The View From Everywhere," which allows for substantive, fundamental moral disagreement. This stance is used to develop a bargaining model in which agents can cooperate despite seeing different perspectives. Rather than arguing for an ideal contract or particular principles of justice, Muldoon outlines a procedure for iterated revisions to the rules of a social contract. It expands Mill's conception of experiments in living to help form a foundational principle for social contract theory. By embracing this kind of experimentation, we move away from a conception of justice as an end state, and toward a conception of justice as a trajectory. (shrink)

In controversies about technology and society, there is no idea more pro vocative than the notion that technical things have political qualities. At issue is the claim that the machines, structures, and systems of modern material culture can be accurately judged not only for their contributions of efficiency and pro-ductivity, not merely for their positive and negative environmental side effects, but also for the ways in which they can embody specific forms of power and authority. Since ideas of this kind (...) have a persistent and troubling presence in discussions about the meaning of technology, they deserve explicit attention... (shrink)