Law Commons^™

Hunting And Gathering On The Legal Information Savannah, Susan Nevelow Mart, Adam Litzler, David Gunderman

Publications

This article asks, what is it like for novice researchers to research real-world legal problems using four platforms: Bloomberg Law, Fastcase, Lexis Advance, and Westlaw? The study findings produced some surprises, as well as some clear implications for teaching legal research.

Artificial Intelligence Inventions & Patent Disclosure, Tabrez Y. Ebrahim

Faculty Scholarship

Artificial intelligence (“AI”) has attracted significant attention and has imposed challenges for society. Yet surprisingly, scholars have paid little attention to the impediments AI imposes on patent law’s disclosure function from the lenses of theory and policy. Patents are conditioned on inventors describing their inventions, but the inner workings and the use of AI in the inventive process are not properly understood or are largely unknown. The lack of transparency of the parameters of the AI inventive process or the use of AI makes it difficult to enable a future use of AI to achieve the same end state. While …

Inside The Black Box Of Search Algorithms, Susan Nevelow Mart, Joe Breda, Ed Walters, Tito Sierra, Khalid Al-Kofahi

Publications

A behind-the-scenes look at the algorithms that rank results in Bloomberg Law, Fastcase, Lexis Advance, and Westlaw.

Results May Vary, Susan Nevelow Mart

Publications

No abstract provided.

Understanding The Human Element In Search Algorithms And Discovering How It Affects Search Results, 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 …

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/.

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.

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

Publications

No abstract provided.