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Post-hoc interpretation aims to explain a trained model and reveal how the model arrives at a decision. Though research on post-hoc interpretations has developed rapidly, one growing pain in this field is the difficulty in evaluating interpretations. There are some crucial logic traps behind existing evaluation methods, which are ignored by most works. In this opinion piece, we summarize four kinds evaluation methods and point out the corresponding logic traps behind them. We argue that we should be clear about these traps rather than ignore them and draw conclusions assertively.
We address the problem of uncertainty calibration. While standard deep neural networks typically yield uncalibrated predictions, calibrated confidence scores that are representative of the true likelihood of a prediction can be achieved using post-ho
We address the problem of uncertainty calibration and introduce a novel calibration method, Parametrized Temperature Scaling (PTS). Standard deep neural networks typically yield uncalibrated predictions, which can be transformed into calibrated confi
As machine learning black boxes are increasingly being deployed in domains such as healthcare and criminal justice, there is growing emphasis on building tools and techniques for explaining these black boxes in an interpretable manner. Such explanati
Calibration of neural networks is a critical aspect to consider when incorporating machine learning models in real-world decision-making systems where the confidence of decisions are equally important as the decisions themselves. In recent years, the
As black box explanations are increasingly being employed to establish model credibility in high stakes settings, it is important to ensure that these explanations are accurate and reliable. However, prior work demonstrates that explanations generate