63 citations · 65 across the 11 of their papers we have counts for
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cs.HC2021★ 63 cited
Towards Relatable Explainable AI with the Perceptual Process
Wencan Zhang, Brian Y. Lim
Machine learning models need to provide contrastive explanations, since people often seek to understand why a puzzling prediction occurred instead of some expected outcome. Current…
cs.CV2021
Exploiting Explanations for Model Inversion Attacks
Xuejun Zhao, Wencan Zhang, Xiaokui Xiao +1
The successful deployment of artificial intelligence (AI) in many domains from healthcare to hiring requires their responsible use, particularly in model explanations and privacy.…
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…