Welcome to the digital.law repository at the University of Washington

How Copyright Law Can Fix Artificial Intelligence’s Implicit Bias Problem

Show simple item record

dc.contributor
dc.contributor.author Levendowski, Amanda
dc.date.accessioned 2018-06-18T15:07:57Z
dc.date.available 2018-06-18T15:07:57Z
dc.date.issued 2018-06
dc.identifier.citation 93 Wash. L. Rev. 579 (2018) en_US
dc.identifier.issn 0043-0617
dc.identifier.uri http://hdl.handle.net/1773.1/1804
dc.description Volume 93, no.2, June 2018 en_US
dc.description.abstract Amanda Levendowski, Technology Law and Policy Clinical Teaching Fellow, New York University School of Law and Research Fellow, NYU Information Law Institute. As the use of artificial intelligence (AI) continues to spread, we have seen an increase in examples of AI systems reflecting or exacerbating societal bias, from racist facial recognition to sexist natural language processing. These biases threaten to overshadow AI’s technological gains and potential benefits. While legal and computer science scholars have analyzed many sources of bias, including the unexamined assumptions of its oftenhomogenous creators, flawed algorithms, and incomplete datasets, the role of the law itself has been largely ignored. Yet just as code and culture play significant roles in how AI agents learn about and act in the world, so too do the laws that govern them. This Article is the first to examine perhaps the most powerful law impacting AI bias: copyright. Artificial intelligence often learns to “think” by reading, viewing, and listening to copies of human works. This Article first explores the problem of bias through the lens of copyright doctrine, looking at how the law’s exclusion of access to certain copyrighted source materials may create or promote biased AI systems. Copyright law limits bias mitigation techniques, such as testing AI through reverse engineering, algorithmic accountability processes, and competing to convert customers. The rules of copyright law also privilege access to certain works over others, encouraging AI creators to use easily available, legally low-risk sources of data for teaching AI, even when those data are demonstrably biased. Second, it examines how a different part of copyright law—the fair use doctrine—has traditionally been used to address similar concerns in other technological fields, and asks whether it is equally capable of addressing them in the field of AI bias. The Article ultimately concludes that it is, in large part because the normative values embedded within traditional fair use ultimately align with the goals of mitigating AI bias and, quite literally, creating fairer AI systems. en_US
dc.language.iso en_US en_US
dc.publisher Seattle: Washington Law Review, University of Washington School of Law en_US
dc.subject Article en_US
dc.title How Copyright Law Can Fix Artificial Intelligence’s Implicit Bias Problem en_US
dc.type Article en_US
dc.rights.holder Copyright 2018 by Washington Law Review Association. en_US


Files in this item

This item appears in the following Collection(s)

Show simple item record

Search digital.law


Advanced Search

Browse

My Account