3 citations · 6 across the 4 of their papers we have counts for
5 papers
How to Measure Human-AI Prediction Accuracy in Explainable AI Systems
Sujay Koujalgi, Andrew Anderson, Iyadunni Adenuga +8
Assessing an AI system's behavior-particularly in Explainable AI Systems-is sometimes done empirically, by measuring people's abilities to predict the agent's next move-but how to…
Measuring User Experience Inclusivity in Human-AI Interaction via Five User Problem-Solving Styles
Andrew Anderson, Jimena Noa Guevara, Fatima Moussaoui +3
Motivations: Recent research has emerged on generally how to improve AI product user experiences, but relatively little is known about an AI product's inclusivity. For example, wha…
Explaining Reinforcement Learning to Mere Mortals: An Empirical Study
Andrew Anderson, Jonathan Dodge, Amrita Sadarangani +6
We present a user study to investigate the impact of explanations on non-experts' understanding of reinforcement learning (RL) agents. We investigate both a common RL visualization…
Toward Foraging for Understanding of StarCraft Agents: An Empirical Study
Sean Penney, Jonathan Dodge, Claudia Hilderbrand +3
Assessing and understanding intelligent agents is a difficult task for users that lack an AI background. A relatively new area, called "Explainable AI," is emerging to help address…
How the Experts Do It: Assessing and Explaining Agent Behaviors in Real-Time Strategy Games
Jonathan Dodge, Sean Penney, Claudia Hilderbrand +2
How should an AI-based explanation system explain an agent's complex behavior to ordinary end users who have no background in AI? Answering this question is an active research area…