July 26, 2021
July 26, 2021
July 19, 2022
We are a computer science professor and two librarians who work closely with computer science students. In this paper, we outline the development of an introductory algorithm bias instruction session. As part of our lesson development, we analyzed the results of a survey we conducted of computer science students at three universities on their perceptions about search-engine and big-data algorithms. We examined whether an information literacy component focused on algorithmic bias was beneficial to offer to students in the computational sciences and designed an instructional prototype. We studied qualitative data, including feedback from students and colleagues on our initial instruction module to create the next two modules. We found that students’ reception to the subject of algorithm bias can range from defensive and unaccepting to open and accepting of the existence of such bias. Since the topic ultimately deals with issues of racial, gender-based, and other discrimination, a multidisciplinary approach is needed when teaching about algorithm bias. Our assertion is that librarians have a role in partnering with computer science instructors to ensure that students who major in computer science, who will be the primary creators of algorithms as they enter the workforce, can develop an early awareness and understanding of bias in information systems. Further, when the students receive such training, the automated systems they generate will produce more fair outcomes. Our pedagogy incorporates insights from computer science, library science, medical ethics, and critical theory. The aim of our algorithm bias instruction is to help computer science students recognize and mitigate the systematic marginalization of groups within the current technological environment.
Ramachandran, S., & Cutchin, S. M., & Fu, S. (2021, July), Raising Algorithm Bias Awareness Among Computer Science Students Through Library and Computer Science Instruction Paper presented at 2021 ASEE Virtual Annual Conference Content Access, Virtual Conference. https://peer.asee.org/37634
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