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Explainable artificial intelligence and social science: Further insights for qualitative investigation
Journal article   Open access   Peer reviewed

Explainable artificial intelligence and social science: Further insights for qualitative investigation

Adam J. Johs, Denise E. Agosto and Rosina O. Weber
Applied AI letters, v 3(1), pn/a
Feb 2022
DOI: https://doi.org/10.1002/ail2.64
url
https://doi.org/10.1002/ail2.64View
Published, Version of Record (VoR)CC BY V4.0,  Open

Abstract

explainable artificial intelligence qualitative investigation rigor user studies
We present a scoping review of user studies in explainable artificial intelligence (XAI) entailing qualitative investigation. We draw on social science corpora to suggest ways for improving the rigor of studies where XAI researchers use observations, interviews, focus groups, and/or questionnaire tasks to collect qualitative data. We contextualize the presentation of the XAI papers included in our review according to the components of rigor discussed in the qualitative research literature: (a) underlying theories or frameworks; (b) methodological approaches; (c) data collection methods; and (d) data analysis processes. The results of our review dovetail with calls made by others in the XAI community advocating for collaboration with experts from social disciplines toward bolstering rigor and effectiveness in user studies. We conducted a scoping review of papers in the XAI literature entailing qualitative investigation. Our findings align with those advocating for collaboration with social science experts toward bolstering user studies.

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