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Raising Algorithm Bias Awareness Among Computer Science Students Through Library and Computer Science Instruction

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Conference

2021 ASEE Virtual Annual Conference Content Access

Location

Virtual Conference

Publication Date

July 26, 2021

Start Date

July 26, 2021

End Date

July 19, 2022

Conference Session

Engineering Libraries Division Technical Session 1: Diversity

Tagged Division

Engineering Libraries

Tagged Topic

Diversity

Page Count

24

Permanent URL

https://peer.asee.org/37634

Download Count

86

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Paper Authors

biography

Shalini Ramachandran Boise State University

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Shalini Ramachandran is a Faculty Liaison for Research Development at Boise State University. Prior to this position, she was a Science and Engineering Librarian at the University of Southern California. Her research interests include algorithm bias, information access in higher education, and open access publishing.

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biography

Steven Matthew Cutchin Boise State University

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Dr. Steve Cutchin joined the faculty at Boise State University in August 2013 From 2008 to 2013 he was manager of the KAUST Visualization Laboratory Core Facility and the Supercomputer Facility at King Abdullah’s University of Science and Technology (KAUST) in Thuwal, Saudi Arabia. At KAUST he recruited a technical team of engineers and visualization scientists while managing the building of the state of the art scientific data visualization laboratory on the KAUST campus, forged relationships with international university and corporate partners, continued to improve the laboratory and recruit new staff. Prior to his work in Saudi Arabia, Dr. Cutchin worked at the University of California, San Diego (UCSD) first as manager of Visualization Services at the San Diego Supercomputer Center and later at California Institute for Telecommunications and Information Technology (Calit2). He has worked as a Sr. Software Engineer at Walt Disney Feature Animation developing software tools to improve animation production on feature films. He has published articles on Computer Graphics and Visualization, created animations for Discovery Channel and images for SIGGRAPH and Supercomputing conferences and journals. He received his doctorate from Purdue University in Computer Science.

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Sheree Fu California State University, Los Angeles

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Sheree Fu is the Engineering, Computer Science, and Technology Librarian at California State University, Los Angeles.

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Abstract

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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