Abstract
Recently, information behavior (IB) research has drawn attention to the broader life of information, noting its role in discursive practices around social and organizational identity. We explore information’s role in occupational and professional identity and identification. How information use figures into the ways that individuals become interested in certain professions (and the barriers to entry they experience) can be helpful in developing policy interventions to foster occupational diversity and inclusion, a particular concern in science, technology, engineering, and math (STEM) fields. This paper reports on a qualitative interview study of machine learning (ML) developers, examining their accounts of how they became interested in the ML field, the barriers they experienced when entering the field, and their patterns of information use in these processes. We discuss the implications of our findings, which reveal information use as an organizing principle that simultaneously defines and continually binds a professional community of practice together.
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Acknowledgements
Thank you to study participants; to project collaborators Nathalie Baracaldo Angel and Bryant Chen; and to Roderic Crooks for comments on earlier drafts. All opinions are my own and do not reflect any institutional endorsement.
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Wolf, C.T. (2019). Professional Identity and Information Use: On Becoming a Machine Learning Developer. In: Taylor, N., Christian-Lamb, C., Martin, M., Nardi, B. (eds) Information in Contemporary Society. iConference 2019. Lecture Notes in Computer Science(), vol 11420. Springer, Cham. https://doi.org/10.1007/978-3-030-15742-5_59
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