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Full Paper: Algorithm Bias: Computer Science Student Perceptions Survey

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Conference

Proceedings of the 2020 ASEE PSW Section Conference, canceled

Location

Davis, California

Publication Date

April 30, 2020

Start Date

April 30, 2020

End Date

October 10, 2020

Tagged Topic

Diversity

Page Count

14

Permanent URL

https://peer.asee.org/36036

Download Count

297

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Paper Authors

biography

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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steven matthew cutchin

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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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Karen Howell University of Southern California

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Karen Howell is the Head of Leavey Library at the University of Southern California. Howell has initiated extensive student outreach programs, partnering with student government and other campus units to highlight library programs and services including outreach to international students (many of them engineering students), first-generation college students, students with disabilities, and veteran students. These include popular orientation, student engagement, and stress relief events. She also serves as Faculty Diversity Recruitment Liaison for the University Park Campus libraries at USC, serves on the USC Libraries Diversity, Equity, and Inclusion Working Group, and is the co-author of an online library research guide for resources on Diversity, Equity, and Inclusion.

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Shalini Ramachandran Boise State University

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Shalini Ramachandran is a Faculty Liaison for Research 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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Abstract

In the United States, Google performs over 3.9 million searches per minute. Monthly desktop searches can exceed over 10.7 billion and mobile searches are predicted to grow steadily. Concurrently, recent discourse has raised questions about bias in search engines and big data algorithms. As the information universe becomes increasingly dominated by algorithms, computer scientists and engineers have ethical obligations to create systems that do no harm. In this paper, the authors discuss a survey that was conducted of computer science and computer engineering students perceptions of algorithm bias. The aim of the survey was to gather preliminary data on how students perceive bias within machine learning and search algorithms. Over 700 computer science and computer engineering students from three different institutions participated in the survey from Fall 2018 to Spring 2019. Based on survey results, Google was overwhelmingly the preferred search engine. The participants also predicted that artificial intelligence algorithms will improve over time. The majority of respondents believe that private companies, not government organizations, need to regulate their own artificial intelligence algorithms. On average, computer science and computer engineering students acknowledge that algorithm bias could occur when people create algorithms. The results suggest that students are familiar with search engines and in general agreement on how algorithm bias should be addressed in the future.

The survey results will be used to consider whether an information literacy component focused on algorithm bias would be beneficial to offer to students in the computational sciences and if so, how best to design the instruction. The study describes students’ prior knowledge for educators seeking to increase awareness of algorithm bias. Our hypothesis is that computer science student exposure to the concept of algorithm bias via instruction would create positive changes in the technology workforce as students with training in algorithm bias mitigation bring their knowledge to the sector. A commitment to understanding and reducing algorithm bias in the tech industry would create spaces where communities can optimize their search for information and expect fair treatment from automated systems.

Fu, S., & cutchin, S. M., & Howell, K., & Ramachandran, S. (2020, April), Full Paper: Algorithm Bias: Computer Science Student Perceptions Survey Paper presented at Proceedings of the 2020 ASEE PSW Section Conference, canceled, Davis, California. https://peer.asee.org/36036

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