The field of transparent Machine Learning (ML) has contributed many novel methods aiming at better interpretability for computer vision and ML models in general. But how useful the explanations provided by transparent ML methods are for humans remains difficult to assess. Most studies evaluate interpretability in qualitative comparisons, they use experimental paradigms that do not allow for direct comparisons amongst methods or they report only offline experiments with no humans in the loop. While there are clear advantages of evaluations with no humans in the loop, such as scalability, reproducibility and less algorithmic bias than with humans in the loop, these metrics are limited in their usefulness if we do not understand how they relate to other metrics that take human cognition into account. Here we investigate the quality of interpretable computer vision algorithms using techniques from psychophysics. In crowdsourced annotation tasks we study the impact of different interpretability approaches on annotation accuracy and task time. In order to relate these findings to quality measures for interpretability without humans in the loop we compare quality metrics with and without humans in the loop. Our results demonstrate that psychophysical experiments allow for robust quality assessment of transparency in machine learning. Interestingly the quality metrics computed without humans in the loop did not provide a consistent ranking of interpretability methods nor were they representative for how useful an explanation was for humans. These findings highlight the potential of methods from classical psychophysics for modern machine learning applications. We hope that our results provide convincing arguments for evaluating interpretability in its natural habitat, human-ML interaction, if the goal is to obtain an authentic assessment of interpretability.

在这项研究中，作者们使用基于众包的注释任务和心理物理学技术来评估可解释的计算机视觉算法的透明度质量，与没有人类参与的质量度量进行比较，并发现众包实验提供了一种稳健的质量评估方法。同时，文中提到在解释性方法排名、以及对人类是否有用方面，没有人类参与的质量度量与实际情况不符，为了获得对解释性的真实评估，应该在人类-机器学习交互的自然环境中进行评估。

量化比较可解释计算机视觉模型的心理物理学方法