An iSchool approach to data science: Human‐centered, socially responsible, and context‐driven
Abstract
The Information Schools, also referred to as iSchools, have a unique approach to data science with three distinct components: human‐centeredness, socially responsible, and rooted in context. In this position paper, we highlight and expand on these components and show how they are integrated in various research and educational activities related to data science that are being carried out at iSchools. We argue that the iSchool way of doing data science is not only highly relevant to the current times, but also crucial in solving problems of tomorrow. Specifically, we accentuate the issues of developing insights and solutions that are not only data‐driven, but also incorporate human values, including transparency, privacy, ethics, fairness, and equity. This approach to data science has meaningful implications on how we educate the students and train the next generation of scholars and policymakers. Here, we provide some of those design decisions, rooted in evidence‐based research, along with our perspective on how data science is currently situated and how it should be advanced in iSchools.
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Teachers paid no attention to ethical concerns such as biased decisions, lack of transparency, privacy issues, and the risk of invasion of privacy.
“which found that teachers paid no attention to ethical concerns, such as biased decisions, lack of transparency, privacy issues, and the risk of invasion of privacy [52,53].”
From Developing AI Literacy for Primary and Middle School Teachers in China: Based on a Structural Equation Modeling Analysis (2022) · cites this paper as [53]
Cites
Steps toward an ecology of infrastructure: Design and access for large information spaces
Star, S. L., & Ruhleder, K. (1996) · Information Systems Research · doi:10.1287/isre.7.1.111 · Cited by 3,238 · see citing works
Personalization of knowledge, personal knowledge ecology, and digital nomadism
Jarrahi, M. H., Philips, G., Sutherland, W., Sawyer, S., & Erickson, I. (2019) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.24134 · Cited by 82 · see citing works
Social Informatics: Past, Present and Future
(2015) · Online Information Review · doi:10.1108/oir-04-2015-0124 · Cited by 49 · see citing works
A Hands-On Introduction to Data Science
Shah, C. S. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108560412 · Cited by 28 · see citing works
Self-citationData science education in the iSchool context
Oh, S. G., Song, I., Mostafa, J., et al. (2019) · Proceedings of the Association for Information Science and Technology · doi:10.1002/pra2.90 · Cited by 9 · see citing works
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Zhao, L., Wu, X., & Luo, H. (2022) · Sustainability · doi:10.3390/su142114549 · Cited by 239 · see citing works
From Bias to Repair: Error as a Site of Collaboration and Negotiation in Applied Data Science Work
Lin, C. K., & Jackson, S. J. (2023) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3579607 · Cited by 22 · see citing works
- Authors
- Shah, C., Anderson, T., Hagen, L., & Zhang, Y.
- Affiliations
- University of Washington (US)
- Ethics for AI & ADM Sydney Australia
- University of South Florida (US)
- Kent State University (US)
- Corresponding author
- Shah, Chirag
- Venue
- Journal of the Association for Information Science and Technology
- Publisher
- Wiley
- Published
- 2021-01-22
- Volume
- 72
- Issue
- 6
- Pages
- 793-796
- ISSN
- 2330-1635 (ISSN-L)
- 2330-1643
- OpenAlex
- W3122139803
- Cited by
- 29
- References
- 5
- Citations by year
- 2026: 2 · 2025: 7 · 2024: 2 · 2023: 8 · 2022: 8 · 2021: 2
- FWCI
- 6.25
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of the Association for Information Science and Technology (publishedVersion)
- Digital Commons - University of South Florida (University of South Florida) (submittedVersion)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Library and Information Sciences › Library Science and Information Literacy
- Physical Sciences › Computer Science › Information Systems › Information Systems Education and Curriculum Development
- Keywords
- iSchools, data science, social responsibility, data ethics, privacy, transparency, equity and fairness, data science education
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