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Qualitative investigation in explainable artificial intelligence: A bit more insight from social science
  • Adam Johs,
  • Denise Agosto,
  • Rosina Weber
Adam Johs
Drexel University College of Information Science and Technology

Corresponding Author:ajj37@drexel.edu

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Denise Agosto
Drexel University College of Information Science and Technology
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Rosina Weber
Drexel University College of Information Science and Technology
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Abstract

We present a focused analysis 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 questionnaires to capture qualitative data. We contextualize the presentation of the XAI papers included in our analysis according to the components of rigor described in the qualitative research literature: 1) underlying theories or frameworks, 2) methodological approaches, 3) data collection methods, and 4) data analysis processes. The results of our analysis support calls from others in the XAI community advocating for collaboration with experts from social disciplines to bolster rigor and effectiveness in user studies.
20 Sep 2021Submitted to Applied AI Letters
21 Sep 2021Submission Checks Completed
21 Sep 2021Assigned to Editor
28 Sep 2021Reviewer(s) Assigned
22 Oct 2021Review(s) Completed, Editorial Evaluation Pending
22 Oct 2021Editorial Decision: Revise Major
09 Dec 20211st Revision Received
10 Dec 2021Submission Checks Completed
10 Dec 2021Assigned to Editor
10 Dec 2021Reviewer(s) Assigned
22 Dec 2021Review(s) Completed, Editorial Evaluation Pending
12 Jan 2022Editorial Decision: Accept