Predictability and Comprehensibility in Post-Hoc XAI Methods: A User-Centered Analysis

Anahid Naghibzadeh-Jalali, Haslhofer Bernhard, Simone Kriglstein, Andreas Rauber

Publikation: Beitrag in Buch oder TagungsbandVortrag mit Beitrag in TagungsbandBegutachtung


Post-hoc explainability methods aim to clarify predictions of black-box machine learning models. However, it is still largely unclear how well users comprehend the provided explanations and whether these increase the users’ ability to predict the model behavior. We approach this question by conducting a user study to evaluate comprehensibility and predictability in two widely used tools: LIME and SHAP. Moreover, we investigate the effect of counterfactual explanations and misclassifications on users’ ability to understand and predict the model behavior. We find that the comprehensibility of SHAP is significantly reduced when explanations are provided for samples near a model’s decision boundary. Furthermore, we find that counterfactual explanations and misclassifications can significantly increase the users’ understanding of how a machine learning model is making decisions. Based on our findings, we also derive design recommendations for future post-hoc explainability methods with increased comprehensibility and predictability.
TitelIntelligent Computing. SAI 2023
UntertitelProceedings of the 2023 Computing Conference, Volume 1
Redakteure/-innenKohei Arai
ISBN (elektronisch)978-3-031-37717-4
PublikationsstatusVeröffentlicht - Sept. 2023
VeranstaltungComputing Conference 2023 - Clayton Hotel Chiswick, London, Großbritannien/Vereinigtes Königreich
Dauer: 22 Juni 202323 Juni 2023


NameLecture Notes in Networks and Systems (LNNS)


KonferenzComputing Conference 2023
Land/GebietGroßbritannien/Vereinigtes Königreich

Research Field

  • Experience Business Transformation
  • Data Science


  • eXplainable Artificial Intelligence
  • Machine Learning


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