… model dynamics and its impact on explanation. In this paper, we perform a focused study
on the impact of model interpretability in the context of calibration… confident and under-confident …

… This paper investigates the effects of calibrating the underlying models in explanation …
calibrated model results in better calibrated explanations. Furthermore, since better calibration …

… importance explanation method, Calibrated Explanations, … benefits of Calibrated Explanations,
such as calibration of the … model and calibration set is used, the resulting explanation …

… model is leveraging, even without access to model internal representations. As shown in
Figure 1, we perform calibration by connecting model … reasoning” of the model. For the question …

… The unreliability of feature weights, often skewed due to poorly calibrated ML models, …
importance explanation method presented in this paper, called Calibrated Explanations (CE…

… The expected calibration errors are further reduced when combined … model explanations
can help calibrate posterior estimates. … model explanations are useful for calibrating black-box …

… In order to calibrate our models, we focus on post-hoc calibration, which can be applied to
… One possible explanation for this improvement is that calibration improves the stability of the …

… It includes a study of estimators of key quantities, explanations of inherent subtleties of …
aspect of model calibration. If a model is calibrated, then every induced calibration function is …

… This paper introduces Fast Calibrated Explanations, a … , uncertainty-aware explanations for
machine learning models. By … explanation framework—into the core elements of Calibrated …

… one fairness solution would prove under a different definition of fairness. For example,
well-calibrated models can be considered fair since the calibration level in a model is defined as a …