Algorithm Bias: Instruction, Reflection, and Advocacy
In this presentation, we discuss our ongoing library instruction on algorithm bias to computer science students. We have instructed students about algorithm bias at three universities, with varying demographics, two public and one private. Algorithm bias is a persistent problem in the technology industry and negatively impacts people based on gender, race, and other categories. As systems that determine outcomes for health, employment, education, and incarceration become automated, the impact of machine-based bias can be felt by large segments in society. We are interested in teaching computer students about algorithm bias as many of them are going to become programmers of automated systems and search engines. We are a computer science professor and two engineering librarians. Our presentation will cover our observations gathered from instructing students and also discuss some of the reasons why algorithm bias can be difficult to root out. We argue that without a commitment to open technologies as well as external regulatory checks, algorithm bias will continue to persist within the tech industry. Librarians’ strong advocacy for inclusion and equity in information dissemination coupled with their expertise in information theory makes them strong allies in the quest to mitigate harm caused by algorithm bias.
