most citedShow or Suppress? Managing Input Uncertainty in Machine Learning Model Explanations

2 citations · 2 across the 2 of their papers we have counts for

collaborators

2 papers

cs.HC2022

Debiased-CAM to mitigate systematic error with faithful visual explanations of machine learning

Wencan Zhang, Mariella Dimiccoli, Brian Y. Lim

Model explanations such as saliency maps can improve user trust in AI by highlighting important features for a prediction. However, these become distorted and misleading when expla…

cs.LG2021★ 2 cited

Show or Suppress? Managing Input Uncertainty in Machine Learning Model Explanations

Danding Wang, Wencan Zhang, Brian Y. Lim

Feature attribution is widely used in interpretable machine learning to explain how influential each measured input feature value is for an output inference. However, measurements…