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Contrastive Explanations That Anticipate Human Misconceptions Can Improve Human Decision-Making Skills
People's decision-making abilities often fail to improve or may even erode when they rely on AI for decision-support, even when the AI provides informative explanations.
Learning Interpretable Fair Representations
Numerous approaches have been recently proposed for learning fair representations that mitigate unfair outcomes in prediction tasks.
Towards Optimizing Human-Centric Objectives in AI-Assisted Decision-Making With Offline Reinforcement Learning
Across two experiments (N=316 and N=964), our results demonstrated that people interacting with policies optimized for accuracy achieve significantly better accuracy -- and even human-AI complementarity -- compared to those interacting with any other type of AI support.
Accuracy-Time Tradeoffs in AI-Assisted Decision Making under Time Pressure
The precise benefit can depend on both the user and task.
How Different Groups Prioritize Ethical Values for Responsible AI
Compared to the US-representative sample, AI practitioners appear to consider responsible AI values as less important and emphasize a different set of values.
To Trust or to Think: Cognitive Forcing Functions Can Reduce Overreliance on AI in AI-assisted Decision-making
To audit our work for intervention-generated inequalities, we investigated whether our interventions benefited equally people with different levels of Need for Cognition (i. e., motivation to engage in effortful mental activities).
Proxy Tasks and Subjective Measures Can Be Misleading in Evaluating Explainable AI Systems
The results of our experiments demonstrate that evaluations with proxy tasks did not predict the results of the evaluations with the actual decision-making tasks.
