116 citations · 229 across the 10 of their papers we have counts for
6 papers · 1 filter
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…
SalienTrack: providing salient information for semi-automated self-tracking feedback with model explanations
Yunlong Wang, Jiaying Liu, Homin Park +4
Self-tracking can improve people's awareness of their unhealthy behaviors and support reflection to inform behavior change. Increasingly, new technologies make tracking easier, lea…
Interpretable Directed Diversity: Leveraging Model Explanations for Iterative Crowd Ideation
Yunlong Wang, Priyadarshini Venkatesh, Brian Y. Lim
Feedback in creativity support tools can help crowdworkers to improve their ideations. However, current feedback methods require human assessment from facilitators or peers. This i…
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.…
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…
Directed Diversity: Leveraging Language Embedding Distances for Collective Creativity in Crowd Ideation
Samuel Rhys Cox, Yunlong Wang, Ashraf Abdul +2
Crowdsourcing can collect many diverse ideas by prompting ideators individually, but this can generate redundant ideas. Prior methods reduce redundancy by presenting peers' ideas o…