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

D 2023

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

JALALI, Anahid, Bernhard HASLHOFER, Simone KRIGLSTEIN and Andreas RAUBER

Basic information

Original name

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

Authors

JALALI, Anahid, Bernhard HASLHOFER, Simone KRIGLSTEIN and Andreas RAUBER

Edition

Intelligent Computing, 2023

Publisher

Springer

Other information

Language

English

Type of outcome

Stať ve sborníku

Confidentiality degree

není předmětem státního či obchodního tajemství

References:

URL

Organization unit

Faculty of Informatics

DOI

http://dx.doi.org/10.1007/978-3-031-37717-4_46

Keywords (in Czech)

eXplainable Artificial Intelligence, Machine Learning Interpretability,Human Computer Interaction

Keywords in English

eXplainable Artificial Intelligence, Machine Learning Interpretability,Human Computer Interaction

Abstract

V originále

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.

Citovat

JALALI, Anahid, Bernhard HASLHOFER, Simone KRIGLSTEIN and Andreas RAUBER. Predictability and Comprehensibility in Post-Hoc XAI Methods: A User-Centered Analysis. In Arai, K. Intelligent Computing. Springer, 2023. Available from: https://dx.doi.org/10.1007/978-3-031-37717-4_46.

@inproceedings{2307097,
   author = {Jalali, Anahid and Haslhofer, Bernhard and Kriglstein, Simone and Rauber, Andreas},
   booktitle = {Intelligent Computing},
   doi = {http://dx.doi.org/10.1007/978-3-031-37717-4_46},
   editor = {Arai, K.},
   keywords = {eXplainable Artificial Intelligence, Machine Learning Interpretability,Human Computer Interaction},
   language = {eng},
   publisher = {Springer},
   title = {Predictability and Comprehensibility in Post-Hoc XAI Methods: A User-Centered Analysis},
   url = {https://link.springer.com/chapter/10.1007/978-3-031-37717-4_46},
   year = {2023}
}

TY  - JOUR
ID  - 2307097
AU  - Jalali, Anahid - Haslhofer, Bernhard - Kriglstein, Simone - Rauber, Andreas
PY  - 2023
TI  - Predictability and Comprehensibility in Post-Hoc XAI Methods: A User-Centered Analysis
PB  - Springer
KW  - eXplainable Artificial Intelligence, Machine Learning Interpretability,Human Computer Interaction
UR  - https://link.springer.com/chapter/10.1007/978-3-031-37717-4_46
N2  - 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.
ER  -

JALALI, Anahid, Bernhard HASLHOFER, Simone KRIGLSTEIN and Andreas RAUBER. Predictability and Comprehensibility in Post-Hoc XAI Methods: A User-Centered Analysis. In Arai, K. \textit{Intelligent Computing}. Springer, 2023. Available from: https://dx.doi.org/10.1007/978-3-031-37717-4\_{}46.

Detailed Information on Publication Record