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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 can be uncertain, and it is unclear how the awareness of input uncertainty can affect the trust in explanations. We propose and study two approaches to help users to manage their perception of uncertainty in a model explanation: 1) transparently show uncertainty in feature attributions to allow users to reflect on, and 2) suppress attribution to features with uncertain measurements and shift attribution to other features by regularizing with an uncertainty penalty. Through simulation experiments, qualitative interviews, and quantitative user evaluations, we identified the benefits of moderately suppressing attribution uncertainty, and concerns regarding showing attribution uncertainty. This work adds to the understanding of handling and communicating uncertainty for model interpretability.

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The daily practice of online image sharing enriches our lives, but also raises a severe issue of privacy leakage. To mitigate the privacy risks during image sharing, some researchers modify the sensitive elements in images with visual obfuscation methods including traditional ones like blurring and pixelating, as well as generative ones based on deep learning. However, images processed by such methods may be recovered or recognized by models, which cannot guarantee privacy. Further, traditional methods make the images very unnatural with low image quality. Although generative methods produce better images, most of them suffer from insufficiency in the frequency domain, which influences image quality. Therefore, we propose the AdvERsArial Sensitive Element Remover (ERASER) to guarantee both image privacy and image quality. 1) To preserve image privacy, for the regions containing sensitive elements, ERASER guarantees enough difference after being modified in an adversarial way. Specifically, we take both the region and global content into consideration with a Prior Transformer and obtain the corresponding region prior and global prior. Based on the priors, ERASER is trained with an adversarial Difference Loss to make the content in the regions different. As a result, ERASER can reserve the main structure and change the texture of the target regions for image privacy preservation. 2) To guarantee the image quality, ERASER improves the frequency insufficiency of current generative methods. Specifically, the region prior and global prior are processed with Fast Fourier Convolution to capture characteristics and achieve consistency in both pixel and frequency domains. Quantitative analyses demonstrate that the proposed ERASER achieves a balance between image quality and image privacy preservation, while qualitative analyses demonstrate that ERASER indeed reduces the privacy risk from the visual perception aspect.