Law Commons^™

Artificial Intelligence In Health Care: Applications And Legal Implications, W. Nicholson Price Ii

Articles

Artificial intelligence (AI) is rapidly moving to change the healthcare system. Driven by the juxtaposition of big data and powerful machine learning techniques—terms I will explain momentarily—innovators have begun to develop tools to improve the process of clinical care, to advance medical research, and to improve efficiency. These tools rely on algorithms, programs created from healthcare data that can make predictions or recommendations. However, the algorithms themselves are often too complex for their reasoning to be understood or even stated explicitly. Such algorithms may be best described as “black-box.” This article briefly describes the concept of AI in medicine, including …

Data For The Algorithm As A Human Artifact: Implications For Legal [Re]Search, Susan Nevelow Mart

Research Data

These documents underlie and are cited in this empirical study: Susan Nevelow Mart, The Algorithm as a Human Artifact: Implications for Legal [Re]Search, 109 Law Libr. J. 387, 409 n.123 (2017), available at http://scholar.law.colorado.edu/articles/755/.

The ZIP file contains three files: one PDF document ("Tables for Charts 1-3"), and two SPSS files ("Data Archive" and "Syntax Archive" (SPSS version 24)). The "Syntax Archive" file may be viewed in a text editor (e.g., Notepad) as well as in SPSS.

Appendix B: The Algorithm As A Human Artifact: Implications For Legal [Re]Search, Susan Nevelow Mart

Research Data

This document, "Search Instructions for Algorithm Study," is an electronic Appendix B to, and is cited in, the empirical study: Susan Nevelow Mart, The Algorithm as a Human Artifact: Implications for Legal [Re]Search, 109 Law Libr. J. 387, 400 n.78 (2017), available at http://scholar.law.colorado.edu/articles/755/.

Data-Driven Discrimination At Work, Pauline T. Kim

AI-DR Collection

A data revolution is transforming the workplace. Employers are increasingly relying on algorithms to decide who gets interviewed, hired, or promoted. Although data algorithms can help to avoid biased human decision-making, they also risk introducing new sources of bias. Algorithms built on inaccurate, biased, or unrepresentative data can produce outcomes biased along lines of race, sex, or other protected characteristics. Data mining techniques may cause employment decisions to be based on correlations rather than causal relationships; they may obscure the basis on which employment decisions are made; and they may further exacerbate inequality because error detection is limited and feedback …

Anticipating Endangerment: The Biopolitics Of Threatened Species Lists, Irus Braverman

Journal Articles

The last two decades have witnessed an explosion of national and global lists of threatened and endangered species. This article draws on interviews with prominent list managers and observations of their assessments to explore the scientific practices of list-making in the context of species conservation. Delving into the complex calculations of risk and threat that take place in the process of ranking nonhuman species based on their probability of extinction, the article explores the threatened species list as a biopolitical technology of catastrophe governance. My focus on two prominent lists — the IUCN Red List of Threatened Species and NatureServe’s …

Rise Of The Digital Regulator, Rory Van Loo

Faculty Scholarship

The administrative state is leveraging algorithms to influence individuals’ private decisions. Agencies have begun to write rules to shape for-profit websites such as Expedia and have launched their own online tools such as the Consumer Financial Protection Bureau’s mortgage calculator. These digital intermediaries aim to guide people toward better schools, healthier food, and more savings. But enthusiasm for this regulatory paradigm rests on two questionable assumptions. First, digital intermediaries effectively police consumer markets. Second, they require minimal government involvement. Instead, some for-profit online advisers such as travel websites have become what many mortgage brokers were before the 2008 financial crisis. …

Authorship, Disrupted: Ai Authors In Copyright And First Amendment Law, Margot E. Kaminski

Publications

Technology is often characterized as an outside force, with essential qualities, acting on the law. But the law, through both doctrine and theory, constructs the meaning of the technology it encounters. A particular feature of a particular technology disrupts the law only because the law has been structured in a way that makes that feature relevant. The law, in other words, plays a significant role in shaping its own disruption. This Essay is a study of how a particular technology, artificial intelligence, is framed by both copyright law and the First Amendment. How the algorithmic author is framed by these …

Internet Safe Harbors And The Transformation Of Copyright Law, Matthew Sag

Faculty Publications & Other Works

This Article explores the potential displacement of substantive copyright law in the increasingly important online environment. In 1998, Congress enacted a system of intermediary safe harbors as part of the Digital Millennium Copyright Act (DMCA). The internet safe harbors and the associated system of notice-and-takedown fundamentally changed the incentives of platforms, users, and rightsholders in relation to claims of copyright infringement. These different incentives interact to yield a functional balance of copyright online that diverges markedly from the experience of copyright law in traditional media environments. More recently, private agreements between rightsholders and large commercial internet platforms have been made …

Every Algorithm Has A Pov, Susan Nevelow Mart

Publications

When legal researchers search in online databases for the information they need to solve a legal problem, they need to remember that the algorithms that are returning results to them were designed by humans. The world of legal research is a human-constructed world, and the biases and assumptions the teams of humans that construct the online world bring to the task are imported into the systems we use for research. This article takes a look at what happens when six different teams of humans set out to solve the same problem: how to return results relevant to a searcher’s query …

The Algorithm As A Human Artifact: Implications For Legal [Re]Search, Susan Nevelow Mart

Publications

The results of using the search algorithms in Westlaw, Lexis Advance, Fastcase, Google Scholar, Ravel, and Casetext are compared. Six groups of humans created six different algorithms, and the results are a testament to the variability of human problem solving. That variability has implications both for researching and teaching research.

Data-Driven Discrimination At Work, Pauline Kim

Scholarship@WashULaw

A data revolution is transforming the workplace. Employers are increasingly relying on algorithms to decide who gets interviewed, hired, or promoted. Although data algorithms can help to avoid biased human decision-making, they also risk introducing new sources of bias. Algorithms built on inaccurate, biased, or unrepresentative data can produce outcomes biased along lines of race, sex, or other protected characteristics. Data mining techniques may cause employment decisions to be based on correlations rather than causal relationships; they may obscure the basis on which employment decisions are made; and they may further exacerbate inequality because error detection is limited and feedback …

Auditing Algorithms For Discrimination, Pauline Kim

Scholarship@WashULaw

This Essay responds to the argument by Joshua Kroll, et al., in Accountable Algorithms, 165 U.PA.L.REV. 633 (2017), that technical tools can be more effective in ensuring the fairness of algorithms than insisting on transparency. When it comes to combating discrimination, technical tools alone will not be able to prevent discriminatory outcomes. Because the causes of bias often lie, not in the code, but in broader social processes, techniques like randomization or predefining constraints on the decision-process cannot guarantee the absence of bias. Even the most carefully designed systems may inadvertently encode preexisting prejudices or reflect structural bias. For this …

Research Algorithms Have A Point Of View: The Effect Of Human Decision Making On Your Search Results, Susan Nevelow Mart

Publications

No abstract provided.