Classification Metrics for Image Explanations: Towards Building Reliable XAI-Evaluations
<|reference_start|>Classification Metrics for Image Explanations: Towards Building Reliable XAI-Evaluations: Decision processes of computer vision models - especially deep neural networks - are opaque in nature, meaning that these decisions cannot be understood by humans. Thus, over the last years, many methods to provide human-understandable explanations have been proposed. For image classification, the most common group are saliency methods, which provide (super-)pixelwise feature attribution scores for input images. But their evaluation still poses a problem, as their results cannot be simply compared to the unknown ground truth. To overcome this, a slew of different proxy metrics have been defined, which are - as the explainability methods themselves - often built on intuition and thus, are possibly unreliable. In this paper, new evaluation metrics for saliency methods are developed and common saliency methods are benchmarked on ImageNet. In addition, a scheme for reliability evaluation of such metrics is proposed that is based on concepts from psychometric testing. The used code can be found at https://github.com/lelo204/ClassificationMetricsForImageExplanations .<|reference_end|>
@article{fresz2024classification,
    title={Classification Metrics for Image Explanations: Towards Building Reliable
  XAI-Evaluations},
    author={Benjamin Fresz, Lena L"orcher, Marco Huber},
    journal={FAccT'24: The 2024 ACM Conference on Fairness, Accountability, and
  Transparency, June 2024, Pages 1-19},
    year={2024},
    doi={10.1145/3630106.3658537},
    archivePrefix={arXiv},
    eprint={2406.05068},
    primaryClass={cs.CV cs.AI cs.HC}
}