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Georgetown University Law Center

About: Georgetown University Law Center is a(n) based out in . It is known for research contribution in the topic(s): Supreme court & Global health. The organization has 585 authors who have published 2488 publication(s) receiving 36650 citation(s). The organization is also known as: Georgetown Law & GULC.


Papers
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Journal ArticleDOI
Abstract: 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 dataAddressing 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.”

1,046 citations

Posted Content
TL;DR: It is necessary to respond to the surprising failure of anonymization, and this Article provides the tools to do so.
Abstract: Computer scientists have recently undermined our faith in the privacy-protecting power of anonymization, the name for techniques for protecting the privacy of individuals in large databases by deleting information like names and social security numbers. These scientists have demonstrated they can often 'reidentify' or 'deanonymize' individuals hidden in anonymized data with astonishing ease. By understanding this research, we will realize we have made a mistake, labored beneath a fundamental misunderstanding, which has assured us much less privacy than we have assumed. This mistake pervades nearly every information privacy law, regulation, and debate, yet regulators and legal scholars have paid it scant attention. We must respond to the surprising failure of anonymization, and this Article provides the tools to do so.

872 citations

Journal ArticleDOI
25 Feb 2020-JAMA
Abstract: On December 31, 2019, China reported to the World Health Organization (WHO) cases of pneumonia in Wuhan, Hubei Province, China, now designated 2019-nCoV. Mounting cases and deaths pose major public health and governance challenges. China’s imposition of an unprecedented cordon sanitaire (a guarded area preventing anyone from leaving) in Hubei Province has also sparked controversy concerning its implementation and effectiveness. Cases have now spread to 4 continents. We describe the current status of 2019-nCoV, assess the response, and offer proposals for bringing the outbreak under control.

703 citations

Journal ArticleDOI
TL;DR: The goal is to review the state of the art in neurocognitive enhancement, its attendant social and ethical problems, and the ways in which society can address these problems.
Abstract: Our growing ability to alter brain function can be used to enhance the mental processes of normal individuals as well as to treat mental dysfunction in people who are ill The prospect of neurocognitive enhancement raises many issues about what is safe, fair and otherwise morally acceptable This article resulted from a meeting on neurocognitive enhancement that was held by the authors Our goal is to review the state of the art in neurocognitive enhancement, its attendant social and ethical problems, and the ways in which society can address these problems

550 citations

Journal ArticleDOI
Abstract: Scientists from various disciplines have begun to focus attention on the psychology and biology of human morality. One research program that has recently gained attention is universal moral grammar (UMG). UMG seeks to describe the nature and origin of moral knowledge by using concepts and models similar to those used in Chomsky's program in linguistics. This approach is thought to provide a fruitful perspective from which to investigate moral competence from computational, ontogenetic, behavioral, physiological and phylogenetic perspectives. In this article, I outline a framework for UMG and describe some of the evidence that supports it. I also propose a novel computational analysis of moral intuitions and argue that future research on this topic should draw more directly on legal theory.

529 citations


Authors

Showing all 585 results

NameH-indexPapersCitations
Lawrence O. Gostin7587923066
Michael J. Saks381555398
Chirag Shah343415056
Sara J. Rosenbaum344256907
Mark Dybul33614171
Steven C. Salop3312011330
Joost Pauwelyn321543429
Mark Tushnet312674754
Gorik Ooms291243013
Alicia Ely Yamin291222703
Julie E. Cohen28632666
James G. Hodge272252874
John H. Jackson271022919
Margaret M. Blair26754711
William W. Bratton251122037
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202174
2020146
2019115
2018113
2017109
2016118