Computer Law Commons^™

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/.

The Racist Algorithm?, Anupam Chander

Michigan Law Review

Review of The Black Box Society: The Secret Algorithms That Control Money and Information by Frank Pasquale.

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 …

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 …

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

Publications

No abstract provided.

Leveraging Predictive Policing Algorithms To Restore Fourth Amendment Protections In High-Crime Areas In A Post-Wardlow World, Kelly K. Koss

Chicago-Kent Law Review

Rapid technological changes have led to an explosion in Big Data collection and analysis through complex computerized algorithms. Law enforcement has not been immune to these technological developments. Many local police departments are now using highly advanced predictive policing technologies to predict when and where crime will occur in their communities, and to allocate crime-fighting resources based on these predictions.

Although predictive policing technology has an array of the potential uses, the scope of this Note is limited to addressing how the statistical outputs from these technologies can be used to restore eroded Fourth Amendment rights in alleged high-crime areas. …

Taking A Byte Out Of Abusive Agency Discretion: A Proposal For Disclosure In The Use Of Computer Models, John P. Barker

University of Michigan Journal of Law Reform

This Note examines the need for comprehensive requirements for the release of information pertaining to the use of computer-generated simulations used by federal administrative agencies or parties appearing before regulatory bodies. Part I of this Note defines computer models, identifies some of their current uses in administrative proceedings and describes the advantages of these models. Part II reviews the current requirements for documentation of computer models and the judicial review standards for agency findings. Part III examines the potential problems in the use of models and discusses the need for more adequate disclosure. Part IV describes several tests for verifying …