Comparing Effects of Attribution-based, Example-based, and Feature-based Explanation Methods on AI-Assisted Decision-Making

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Abstract

Trust calibration is essential in AI-assisted decision-making tasks. If human users understand the reasons for a prediction of an AI model, they can assess whether or not the prediction is reasonable. Especially for high-risk tasks like mushroom hunting (where a wrong decision may be fatal), it is important that users trust or overrule the AI in the right situations. Various explainable AI methods are currently being discussed as potentially useful for facilitating understanding and to calibrate user trust. So far, however, it is unclear which approaches are most effective. Our work takes on this issue; in a between-subjects experiment with 𝑁 = 501 participants. Participants were tasked to classify the edibility of mushrooms depicted on images. We compare the effects of three XAI methods on human AI-assisted decision-making behavior: (i) Grad-CAM attributions; (ii) nearest neighbor examples; and (iii) an adoption of network dissection. For nearest neighbor examples, we found a statistically significant improvement in user performance compared to a condition without explanations. Effects did not reach statistical significance for Grad-CAM and network dissection. For the latter, however, the effect size estimators show a similar tendency as for nearest neighbor. We found that the effects also varied for different task items (i.e., mushroom images). Explanations seem to be particularly effective if they reveal possible flaws in case of wrong AI classifications or reassure users in case of correct classifications. Our results suggest that well-established methods might not be as beneficial to end users as expected and that XAI techniques must be chosen carefully in real-world scenarios.


Citation

Christina Humer, Andreas Hinterreiter, Benedikt Leichtmann, Martina Mara, Marc Streit
Comparing Effects of Attribution-based, Example-based, and Feature-based Explanation Methods on AI-Assisted Decision-Making
OSF Preprint, doi:10.31219/osf.io/h6dwz, 2022.

BibTeX

@article{,
    title = {Comparing Effects of Attribution-based, Example-based, and Feature-based Explanation Methods on AI-Assisted Decision-Making},
    author = {Christina Humer and Andreas Hinterreiter and Benedikt Leichtmann and Martina Mara and Marc Streit},
    journal = {OSF Preprint},
    doi = {10.31219/osf.io/h6dwz},
    url = {https://doi.org/10.31219/osf.io/h6dwz},
    month = {October},
    year = {2022}
}

Acknowledgements

This work was funded by Johannes Kepler University Linz, Linz Institute of Technology (LIT), the State of Upper Austria, and the Federal Ministry of Education, Science and Research under grant number LIT-2019-7-SEE-117, awarded to MM and MS, the Austrian Science Fund under grant number FWF DFH 23–N, and under the Human-Interpretable Machine Learning project (funded by the State of Upper Austria). We thank Moritz Heckmann for helping with the implementation of the AI Forest - The Schwammerl Hunting Gameand Stefan Eibelwimmer for the graphic design of the game. We thank Dr. Otto Stoik, the members of the Mycological Working Group (MYAG) at the Biology Center Linz, Austria, and the German Mycological Society (DGfM) for providing mushroom images for this study. Finally, we thank Alfio Ventura for helping with the study setup.