Mitigating Algorithmic Bias: Strategies for Addressing Discrimination in Data

Authors

Sonia Gipson Rankin, University of New Mexico - School of LawFollow

Document Type

Article

Publication Date

Summer 2024

Abstract

This article examines the pervasive issue of algorithmic bias, particularly within large language models (LLMs) and the legal system. It argues that unlike simple programming bugs, these biases are deeply ingrained in the design and training data of artificial intelligence (AI) systems. By understanding the historical roots of bias and its realworld consequence across various sectors, we can develop effective strategies to mitigate its impact and ensure AI serves as a tool for progress. Weaving together historical insights, case studies, and forward-looking recommendations, the article aims to equip legal professionals with the knowledge and tools necessary to lead the charge against the perpetuation of bias in AI systems.

Publisher

American Bar Association

Publication Title

The SciTech Lawyer

Volume

20

Issue

4

First Page

26

Last Page

32

Recommended Citation

Sonia Gipson Rankin, Mitigating Algorithmic Bias: Strategies for Addressing Discrimination in Data, SciTech Lawyer, Summer 2024, pages 26-32