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Murray examines how the persistence of an older policy's operative core can manifest a \"discriminatory taint\" in the US. He describes a type of relationship distinguished by the formal and functional continuity of temporally separated policies. Understanding what taint is and how to find it advances antidiscrimination discourse in multiple areas. It can help courts navigate what taint means for adjudication (for example, whether and when they should deem taint purged and how to proceed when it is not), guide nonjudicial decision-making (for example, choosing how to act and justifying said choices), and inform scholarly understanding of wrongful discrimination (both over time and as a generalizable phenomenon).

Advocates of algorithmic techniques like data mining argue that these techniques eliminate human biases from the decision-making process. But an algorithm is only as good as the data it works with. Data is frequently imperfect in ways that allow these algorithms to inherit the prejudices of prior decision makers. In other cases, data may simply reflect the widespread biases that persist in society at large. In still others, data mining can discover surprisingly useful regularities that are really just preexisting patterns of exclusion and inequality. Unthinking reliance on data mining can deny historically disadvantaged and vulnerable groups full participation in society. Worse still, because the resulting discrimination is almost always an unintentional emergent property of the algorithm's use rather than a conscious choice by its programmers, it can be unusually hard to identify the source of the problem or to explain it to a court. This Essay examines these concerns through the lens of American antidiscrimination law—more particularly, through Title VII's prohibition of discrimination in employment. In the absence of a demonstrable intent to discriminate, the best doctrinal hope for data mining's victims would seem to lie in disparate impact doctrine. Case law and the Equal Employment Opportunity Commission's Uniform Guidelines, though, hold that a practice can be justified as a business necessity when its outcomes are predictive of future employment outcomes, and data mining is specifically designed to find such statistical correlations. Unless there is a reasonably practical way to demonstrate that these discoveries are spurious, Title VII would appear to bless its use, even though the correlations it discovers will often reflect historic patterns of prejudice, others' discrimination against members of protected groups, or flaws in the underlying data. Addressing the sources of this unintentional discrimination and remedying the corresponding deficiencies in the law will be difficult technically, difficult legally, and difficult politically. There are a number of practical limits to what can be accomplished computationally. For example, when discrimination occurs because the data being mined is itself a result of past intentional discrimination, there is frequently no obvious method to adjust historical data to rid it of this taint. Corrective measures that alter the results of the data mining after it is complete would tread on legally and politically disputed terrain. These challenges for reform throw into stark relief the tension between the two major theories underlying antidiscrimination law: anticlassification and antisubordination. Finding a solution to big data's disparate impact will require more than best efforts to stamp out prejudice and bias; it will require a wholesale reexamination of the meanings of \"discrimination\" and \"fairness.\"

Exposure to derogatory language about immigrants and minority groups leads to political radicalization and deteriorates intergroup relations. This article addresses the psychological processes responsible for these effects as well as those involved in hate‐speech proliferation in contemporary societies and discusses the factors that constrain its growth. We propose that frequent exposure to hate speech has severe effects on emotional, behavioral, and normative levels. Exposure to hate speech results in empathy being replaced by intergroup contempt as a dominant response to others—it is both a motivator and a consequence of derogatory language. The increased presence of hate speech in one's environment creates a sense of a descriptive norm that allows outgroup derogation. This leads to the erosion of existing antidiscriminatory norms. Finally, through a process of desensitization, hate speech reduces people's ability to recognize the offensive character of such language. Based on empirical evidence from the fields of social psychology and psychology of emotion and aggression, we propose a model that explains the described processes, and we trace the dynamics of this model using an agent‐based modeling approach. We show that the mechanisms potentially effective in constraining hate‐speech proliferation (empathy, norms) are eroded by hate speech itself. We argue that through basic psychological dynamics, societies become more accepting of derogatory language and less accepting of immigrants, as well as religious, ethnic, and sexual minorities.

We show that the U.S. anti-discriminatory laws prohibiting discrimination in the workplace based on sexual orientation and gender identity (i.e. lesbian, gay, bisexual, and transgender (LGBT) identities) spur innovation, which ultimately leads to higher firm performance. We use the Human Rights Campaign's Corporate Equality Index (CEI) of 398 (1592 firm-year observations) U.S. firms between 2011 and 2014, and find a significantly positive relationship between CEI and firm innovation. We also find that an interacting effect of CEI and firm innovation leads to higher firm performance. We use our understanding of Rawls' Theory of Justice and stakeholder theory to show that firms with workplace diversity policies are likely to be more innovative and perform better than those without such policies. Our results are robust to endogeneity, reverse causality and simultaneity issues. Our results will trigger debate in similar markets around the globe on the economic benefits of LGBT workplace diversity policies for firms.

To what extent do countries protect the rights of transgender people? How does this differ from legal protections countries offer sexual orientation minorities? What conditions are beneficial for advancing trans rights? Limitations in data availability and accessibility make answering these types of trans-specific questions difficult. To address this shortcoming, I introduce a new dataset. The Trans Rights Indicator Project (TRIP) provides insight into the legal situations transgender people faced in 173 countries from 2000 to 2021. The dataset currently includes 14 indicators that capture the presence or absence of laws related to criminalization, legal gender recognition, and anti-discrimination protections. I then use this data to discuss the global status of transgender rights throughout the period and compare these trends to sexual orientation rights. Finally, I conclude with a preliminary analysis of three institutional and cultural factors that may help explain variation in transgender rights throughout the world.

This work refocuses research on multiple discrimination and immigration by focusing on \"antidiscrimination\" proposals made from sociological research and social action. While the literature review has covered the international community, with particular attention to what has been produced from the social sciences; the other empirical materials (documents available on institutional virtual sites, NGO, etc., as well as ex novo or archival interviews) have preferably been confined to the Spanish case. The inquiry, aimed at Latin, Afro and Muslim associationism, has been complemented by LGTBI, mental health and disability cases, with attention to the concurrence of discrimination for not only migratory diversity. It is concluded with the existence of discursive confluences between analysts and associations, with potential for antidiscriminatory social policies and sociological research.

Policy Points Stigma is an established driver of population‐level health outcomes. Antidiscrimination laws can generate or alleviate stigma and, thus, are a critical component in the study of improving population health. Currently, antidiscrimination laws are often underenforced and are sometimes conceptualized by courts and lawmakers in ways that are too narrow to fully reach all forms of stigma and all individuals who are stigmatized. To remedy these limitations, we propose the creation of a new population‐level surveillance system of antidiscrimination law and its enforcement, a central body to enforce antidiscrimination laws, as well as a collaborative research initiative to enhance the study of the linkages between health and antidiscrimination law in the future. Context Stigma is conceptualized as a fundamental cause of population health inequalities. Antidiscrimination law is one important lever that can influence stigma‐based health inequities, and yet several challenges currently limit the law's potential to address them. Methods To determine whether antidiscrimination law adequately addresses stigma, we compared antidiscrimination law for its applicability to the domains and statuses where stigma is experienced according to the social science literature. To further examine whether law is a sufficient remedy for stigma, we reviewed law literature and government sources for the adequacy of antidiscrimination law enforcement. We also reviewed the law literature for critiques of antidiscrimination law, which revealed conceptual limits of antidiscrimination law that we applied to the context of stigma. Findings In this article, we explored the importance of antidiscrimination law in addressing the population‐level health consequences of stigma and found two key challenges—conceptualization and enforcement—that currently limit its potential. We identified several practical solutions to make antidiscrimination law a more available tool to tackle the health inequities caused by stigma, including (1) the development of a new surveillance system for antidiscrimination laws and their enforcement, (2) an interdisciplinary working group to study the impact of antidiscrimination laws on health, and (3) a central agency tasked with monitoring enforcement of antidiscrimination laws. Conclusions Antidiscrimination law requires better tailoring based on the evidence of who is affected by stigma, as well as where and how stigma occurs, or it will be a poor tool for remedying stigma, regardless of its level of enforcement. Further interdisciplinary research is needed to identify the ways in which law can be crafted into a better tool for redressing the health harms of stigma and to delimit clearer boundaries for when law is and is not the appropriate remedy for these stigma‐induced inequities.

This article presents the first large-scale audit study of discrimination against openly gay men in the United States. Pairs of fictitious résumés were sent in response to 1,769 job postings in seven states. One résumé in each pair was randomly assigned experience in a gay campus organization, and the other résumé was assigned a control organization. Two main findings have emerged. First, in some but not all states, there was significant discrimination against the fictitious applicants who appeared to be gay. This geographic variation in the level of discrimination appears to reflect regional differences in attitudes and antidiscrimination laws. Second, employers who emphasized the importance of stereotypically male heterosexual traits were particularly likely to discriminate against openly gay men. Beyond these particular findings, this study advances the audit literature more generally by covering multiple regions and by highlighting how audit techniques may be used to identify stereotypes that affect employment decisions in real labor markets.

IntroductionPrevious US studies showed that LGBTQ workers played a key role in persuading their employers to include LGBTQ workers as a protected category in antidiscrimination policies. These studies tended to assume that LGBTQ workers are generally supportive of the policy change, but the assumption has not been directly examined. Further, the assumption may be unrealistic for countries where LGBTQ worker activism is uncommon. To address these gaps in the literature, this study investigates how national contexts shape LGBTQ workers’ attitudes toward antidiscrimination policies by comparing the USA and Japan.MethodsThe study analyzes data from in-depth interviews with LGBTQ young adult workers. The data were collected in the USA between 2011 and 2020 (n = 27) and in Japan between 2018 and 2022 (n = 29).ResultsThe analysis reveals that US LGBTQ workers strongly support antidiscrimination policies by expressing a sense of collective identity as LGBTQ people and their trust in formal rules. In contrast, Japanese LGBTQ workers question the necessity and effectiveness of antidiscrimination policies by interpreting policy implementation as employers’ PR performance and by underscoring their need to develop interpersonal trust with colleagues while staying closeted.ConclusionsUS and Japanese workers’ contrasting views on antidiscrimination policies reflect national differences in how LGBTQ people experience and cope with social marginalization in each country.Policy ImplicationsWe make policy recommendations as to how employers and governments in the USA and Japan can support LGBTQ workers while addressing limitations of antidiscrimination policies that LGBTQ workers perceive in each country.

The potential for biases being built into algorithms has been known for some time (e.g., Friedman and Nissenbaum, 1996), yet literature has only recently demonstrated the ways algorithmic profiling can result in social sorting and harm marginalised groups (e.g., Browne, 2015; Eubanks, 2018; Noble, 2018). We contend that with increased algorithmic complexity, biases will become more sophisticated and difficult to identify, control for, or contest. Our argument has four steps: first, we show how harnessing algorithms means that data gathered at a particular place and time relating to specific persons, can be used to build group models applied in different contexts to different persons. Thus, privacy and data protection rights, with their focus on individuals (Coll, 2014; Parsons, 2015), do not protect from the discriminatory potential of algorithmic profiling. Second, we explore the idea that anti-discrimination regulation may be more promising, but acknowledge limitations. Third, we argue that in order to harness anti-discrimination regulation, it needs to confront emergent forms of discrimination or risk creating new invisibilities, including invisibility from existing safeguards. Finally, we outline suggestions to address emergent forms of discrimination and exclusionary invisibilities via intersectional and post-colonial analysis.