April 30, 2020
April 30, 2020
October 10, 2020
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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