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Drawing on millions of court records of eviction cases filed between 2012 and 2016 in 39 states, this study documents the racial and gender demographics of America's evicted population. Black renters received a disproportionate share of eviction filings and experienced the highest rates of eviction filing and eviction judgment. Black and Latinx female renters faced higher eviction rates than their male counterparts. Black and Latinx renters were also more likely to be serially filed against for eviction at the same address. These findings represent the most comprehensive investigation to date of racial and gender disparities among evicted renters in the United States.

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.\"

Big data and Artificial Intelligence (\"AI\") are revolutionizing the ways in which firms, governments, and employers classify individuals. Surprisingly, however, one of the most important threats to anti-discrimination regimes posed by this revolution is largely unexplored or misunderstood in the extant literature. This is the risk that modern algorithms will result in \"proxy discrimination.\" Proxy discrimination is a particularly pernicious subset of disparate impact. Like all forms of disparate impact, it involves a facially neutral practice that disproportionately harms members of a protected class. But a practice producing a disparate impact only amounts to proxy discrimination when the usefulness to the discriminatorofthe facially neutral practice derives, at least in part, from the very fact that it produces a disparate impact. Historically, this occurred when a firm intentionally sought to discriminate against members of a protected class by relying on a proxy for class membership, such as zip code. However, proxy discrimination need not be intentional when membership in a protected class is predictive of a discriminator's facially neutral goal, making discrimination \"rational.\" In these cases, firms may unwittingly proxy discriminate, knowing only that a facially neutral practice produces desirable outcomes. This Article argues that AI and big data are game changers when it comes to this risk of unintentional, but \"rational,\" proxy discrimination. AIs armed with bigdata are inherently structured to engage in proxy discrimination whenever they are deprived of information about membership in a legally suspect class whose predictive power cannot be measured more directly by non-suspect data available to the AI. Simply denying AIs access to the most intuitive proxies for such predictive but suspect characteristics does little to thwart this process; instead it simply causes AIs to locate less intuitive proxies. For these reasons, as AIs become even smarter and big data becomes even bigger, proxy discrimination will represent an increasingly fundamental challenge to anti-discrimination regimes that seek to limit discrimination based on potentially predictive traits. Numerous anti-discrimination regimes do just that, limiting discrimination based on factors like preexisting conditions, genetics, disability, sex, and even race. This Article offers a menu of potential strategies for combatting this risk of proxy discrimination by AIs, including prohibiting the use ofnon-approved types of discrimination, mandating the collection and disclosure of data about impacted individuals' membership in legally protected classes, and requiring firms to employ statistical models that isolate only the predictive power of non-suspect variables.

Police, prosecutors, judges, and other criminal justice actors increasingly use algorithmic risk assessment to estimate the likelihood that a person will commit future crime. As many scholars have noted, these algorithms tend to have disparate racial impacts. In response, critics advocate three strategies of resistance: (1) the exclusion of input factors that correlate closely with race; (2) adjustments to algorithmic design to equalize predictions across racial lines; and (3) rejection of algorithmic methods altogether. This Article's central claim is that these strategies are at best superficial and at worst counterproductive because the source of racial inequality in risk assessment lies neither in the input data, nor in a particular algorithm, nor in algorithmic methodology per se. The deep problem is the nature of prediction itself. All prediction looks to the past to make guesses about future events. In a racially stratified world, any method of prediction will project the inequalities of the past into the future. This is as true of the subjective prediction that has long pervaded criminal justice as it is of the algorithmic tools now replacing it. Algorithmic risk assessment has revealed the inequality inherent in all prediction, forcing us to confront a problem much larger than the challenges of a new technology. Algorithms, in short, shed new light on an old problem. Ultimately, the Article contends, redressing racial disparity in prediction will require more fundamental changes in the way the criminal justice system conceives of and responds to risk. The Article argues that criminal law and policy should, first, more clearly delineate the risks that matter and, second, acknowledge that some kinds of risk may be beyond our ability to measure without racial distortion — in which case they cannot justify state coercion. Further, to the extent that we can reliably assess risk, criminal system actors should strive whenever possible to respond to risk with support rather than restraint. Counterintuitively, algorithmic risk assessment could be a valuable tool in a system that supports the risky.

Access to healthcare data such as electronic health records (EHR) is often restricted by laws established to protect patient privacy. These restrictions hinder the reproducibility of existing results based on private healthcare data and also limit new research. Synthetically-generated healthcare data solve this problem by preserving privacy and enabling researchers and policymakers to drive decisions and methods based on realistic data. Healthcare data can include information about multiple in- and out- patient visits of patients, making it a time-series dataset which is often influenced by protected attributes like age, gender, race etc. The COVID-19 pandemic has exacerbated health inequities, with certain subgroups experiencing poorer outcomes and less access to healthcare. To combat these inequities, synthetic data must “fairly” represent diverse minority subgroups such that the conclusions drawn on synthetic data are correct and the results can be generalized to real data. In this article, we develop two fairness metrics for synthetic data, and analyze all subgroups defined by protected attributes to analyze the bias in three published synthetic research datasets. These covariate-level disparity metrics revealed that synthetic data may not be representative at the univariate and multivariate subgroup-levels and thus, fairness should be addressed when developing data generation methods. We discuss the need for measuring fairness in synthetic healthcare data to enable the development of robust machine learning models to create more equitable synthetic healthcare datasets.

Ensuring that a predictor is not biased against a sensitive feature is the goal of fair learning. Meanwhile, Global Sensitivity Analysis (GSA) is used in numerous contexts to monitor the influence of any feature on an output variable. We merge these two domains, Global Sensitivity Analysis and Fairness, by showing how fairness can be defined using a special framework based on Global Sensitivity Analysis and how various usual indicators are common between these two fields. We also present new Global Sensitivity Analysis indices, as well as rates of convergence, that are useful as fairness proxies.

On January 6, 2021, an estimated two thousand people broke police lines and breached the U.S. Capitol building in an effort to prevent the certification of the 2020 presidential election results. Over one thousand people have been charged with various crimes for their actions that day, from misdemeanor trespassing charges to felony assault with a weapon and seditious conspiracy. Relying on publicly available sources, this Article presents results from an analysis of the first 514 people to have been sentenced in federal court for crimes committed on January 6. The result is a snapshot of the insurrectionists, the charges they faced, and the punishments federal judges imposed on them.

This issue was initiated in a period of global tension and widespread corporate expressions of concern about racism. The articles presented here document the continued presence of disparate racialized experiences in a range of work environments. They provide deep insight into the ways that race shapes the lives of educators, researchers, students, employees, and managers. Such racialized experiences are widely unacknowledged by those whose lives and bodies insulate them. In the time since we initiated this special issue, the anxiety related to talking about race in schools and the workplace has become yet more virulent. In the United States, many municipalities have adopted laws constraining and controlling classroom conversations about race. Corporations continue to struggle with how to implement high-minded DEI policy statements without provoking backlash. We are hopeful that these articles provide greater awareness of racialized capitalism. Further, we aspire to open the door for business ethics research that recognizes the impact of race on institutional policies as well as formal and informal practices. Awareness, recognition, and acknowledgment of disparate impact are essential steps in creating more just work and educational environments.