Systems Love to Hate: Educating Law Students and Practitioners on Bias in AI

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In September 2023, the Algorithmic Accountability Act of 2023 was introduced in Congress to better protect consumers from flawed AI systems that amplify harmful biases. In particular, generative AI, which includes a subcategory of AI that uses raw data to create new content such as images, text, and audio, contains hidden and harmful biases that are significantly impacting the legal field. Legal professionals typically use generative AI to automate time-consuming tasks, including e-discovery, contract drafting, and document review. Now, conversations are arising over whether and how to use AI to set precedent or make “accurate and objectionable” determinations of law. Regardless, use of generative AI allows for the transmission of unconscious biases to permeate the legal field. Bias in AI is recognized nationwide, and we must educate current and future practitioners on the best ways to navigate the developing situation.

After briefly defining differing models of AI, speakers will provide examples of how they train or educate their library users about bias in AI, including studies and advice about best practices to identify and mitigate bias in AI. The discussion will address effective training tactics for using AI in law. Suggestions will be relevant to librarians whose firms are implementing generative AI systems, as well as academic librarians who are preparing students for practice.

Target Audience:

All librarians and legal educators interested in addressing bias and discrimination inherent in legal technologies.

This program is sponsored by the Black Law Librarians SIS (BLL-SIS).