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Dataset resulting from the user study on comprehensibility of explainable AI algorithms.

Szymon Bobek1, Paloma Korycińska2, Monika Krakowska2

  • 1Jagiellonian Human-Centered AI Lab, Mark Kac Center for Complex Systems Research, Institute of Applied Computer Science, Jagiellonian University, Krakow, Poland. szymon.bobek@uj.edu.pl.

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This study introduces a novel dataset on explainable AI (XAI) algorithm comprehensibility. It features interview transcripts from diverse participants, enabling multidisciplinary analysis of AI explanations.

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Area of Science:

  • Artificial Intelligence
  • Human-Computer Interaction
  • Data Science

Background:

  • Explainable AI (XAI) is rapidly developing.
  • Multidisciplinary evaluation of XAI comprehensibility is crucial.
  • Existing datasets lack qualitative, user-centric data on XAI interpretation.

Purpose of the Study:

  • Introduce a comprehensive dataset from a user study on XAI algorithm comprehensibility.
  • Facilitate reproducible research and further analysis in XAI.
  • Enable the study of how different user groups understand AI decisions.

Main Methods:

  • Recruited 149 participants, forming three distinct groups: Mycology Domain Experts (DE), Information Technology students (IT), and Social Sciences and Humanities students (SSH).
  • Collected 39 interview transcripts detailing user interpretation of AI explanations for mushroom classification.
  • Supplemented transcripts with visualizations, thematic analysis, user recommendations, and pre-study surveys on domain knowledge and data literacy.

Main Results:

  • The dataset includes manually tagged transcripts linked to visualizations and analysis results.
  • Participant data covers a range of domain expertise and data analysis literacy.
  • The dataset supports detailed qualitative analysis of user interaction with XAI.

Conclusions:

  • The developed dataset provides a unique resource for evaluating XAI comprehensibility across diverse user groups.
  • Enables further research into the human factors influencing the understanding of AI.
  • Supports the advancement of user-centered XAI design and evaluation.