# Qualitative Investigation in Explainable Artificial Intelligence: A Bit More Insight from Social Science

arXiv: https://arxiv.org/abs/2011.07130 · HTML: https://mlsanity.com/paper/2011.07130/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2020.

## Built on by (most cited first, 2 of 2)

- [Towards a Comprehensive Human-Centred Evaluation Framework for Explainable AI](https://mlsanity.com/paper/2308.06274/) (2023)
- [What's meant by explainable model: A Scoping Review](https://mlsanity.com/paper/2307.09673/) (2023)

## Abstract (arXiv)

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.