A model for data ethics instruction for non-experts

Authors

DOI:

https://doi.org/10.29173/iq1028

Keywords:

data ethics, data literacy, data science, data privacy, algorithmic bias, research impact, community engagement

Abstract

The dramatic increase in use of technological and algorithmic-based solutions for research, economic, and policy decisions has led to a number of high-profile ethical and privacy violations in the last decade. Current disparities in academic curriculum for data and computational science result in significant gaps regarding ethics training in the next generation of data-intensive researchers. Libraries are often called to fill the curricular gaps in data science training for non-data science disciplines, including within the University of California (UC) system. We found that in addition to incomplete computational training, ethics training is almost completely absent in the standard course curricula. In this report, we highlight the experiences of library data services providers in attempting to meet the need for additional training, by designing and running two workshops: Ethical Considerations in Data (2021) and its sequel Data Ethics & Justice (2022). We discuss our interdisciplinary workshop approach and our efforts to highlight resources that can be used by non-experts to engage productively with these topics. Finally, we report a set of recommendations for librarians and data science instructors to more easily incorporate data ethics concepts into curricular instruction.

Author Biographies

Leigh Phan, UCLA Library

Leigh Phan is a Data Scientist at the Data Science Center at University of California, Los Angeles Library.

Ibraheem Ali, University of California, Los Angeles

Ibraheem Ali is the Sciences Data Librarian at the Louise M. Darling Biomedical Library at University of California, Los Angeles.

Stephanie Labou, University of California, San Diego Library

Stephanie Labou is the Data Science Librarian at University of California, San Diego Library.

Erin Foster, University of California, Berkeley

Erin is the Service Lead for the Research Data Management Program at University of California, Berkeley.

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Published

2022-12-28

How to Cite

Phan, L., Ali, I., Labou, S., & Foster, E. (2022). A model for data ethics instruction for non-experts. IASSIST Quarterly, 46(4). https://doi.org/10.29173/iq1028

Issue

Vol. 46 No. 4 (2022): Special Issue - Systemic Racism in Data Practices

Section

Articles

License

Copyright (c) 2022 Leigh Phan, Ibraheem Ali, Stephanie Labou, Erin Foster

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