Human-Centered Evaluation of XAI Methods

Karam Dawoud; Wojciech Samek; Peter Eisert; Sebastian Lapuschkin; Sebastian Bosse

doi:10.1109/icdmw60847.2023.00122

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arXiv · 2310.07534

Human-Centered Evaluation of XAI Methods

Karam Dawoud·Wojciech Samek·Peter Eisert·Sebastian Lapuschkin·Sebastian Bosse

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Abstract

In the ever-evolving field of Artificial Intelligence, a critical challenge has been to decipher the decision-making processes within the so-called "black boxes" in deep learning. Over recent years, a plethora of methods have emerged, dedicated to explaining decisions across diverse tasks. Particularly in tasks like image classification, these methods typically identify and emphasize the pivotal pixels that most influence a classifier's prediction. Interestingly, this approach mirrors human behavior: when asked to explain our rationale for classifying an image, we often point to the most salient features or aspects. Capitalizing on this parallel, our research embarked on a user-centric study. We sought to objectively measure the interpretability of three leading explanation methods: (1) Prototypical Part Network, (2) Occlusion, and (3) Layer-wise Relevance Propagation. Intriguingly, our results highlight that while the regions spotlighted by these methods can vary widely, they all offer humans a nearly equivalent depth of understanding. This enables users to discern and categorize images efficiently, reinforcing the value of these methods in enhancing AI transparency.

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Karam Dawoud, Wojciech Samek, Peter Eisert, Sebastian Lapuschkin, Sebastian Bosse. 2023-10-11. Human-Centered Evaluation of XAI Methods. https://doi.org/10.1109/icdmw60847.2023.00122

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