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Found 6 papers, 0 papers with code
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.
Do People Engage Cognitively with AI? Impact of AI Assistance on Incidental Learning
We hypothesize that learning gains in this condition were due to deeper engagement with explanations needed to arrive at the decisions.
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.
BubbleView: an interface for crowdsourcing image importance maps and tracking visual attention
In this paper, we present BubbleView, an alternative methodology for eye tracking using discrete mouse clicks to measure which information people consciously choose to examine.
