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The field explainable artificial intelligence (XAI) has brought about anarsenal of methods to render Machine Learning (ML) predictions moreinterpretable. But how useful explanations provided by transparent ML methodsare for humans remains difficult to assess. Here we investigate the quality ofinterpretable computer vision algorithms using techniques from psychophysics.In crowdsourced annotation tasks we study the impact of differentinterpretability approaches on annotation accuracy and task time. We comparethese quality metrics with classical XAI, automated quality metrics. Ourresults demonstrate that psychophysical experiments allow for robust qualityassessment of transparency in machine learning. Interestingly the qualitymetrics computed without humans in the loop did not provide a consistentranking of interpretability methods nor were they representative for how usefulan explanation was for humans. These findings highlight the potential ofmethods from classical psychophysics for modern machine learning applications.We hope that our results provide convincing arguments for evaluatinginterpretability in its natural habitat, human-ML interaction, if the goal isto obtain an authentic assessment of interpretability.

Quality Metrics for Transparent Machine Learning With and Without Humans In the Loop Are Not Correlated