Jonathan Dodge

schoolOregon State Universitybar_chart#2573 Global Rank

Total Papers

HCI Rank

#2573

Research Areas

Years Active

Recent Publications

TiiS: After-Action Review for AI (AAR/AI)

Dodge 等人提出 AAR/AI 行动后复盘框架，旨在通过人工智能辅助技术改进决策回顾与反思过程。

IUI '221st AuthorExplainable AI (XAI) +1

How Do People Rank Multiple Mutant Agents?

How might a person decide on which of several AI-powered sequential decision-making systems to rely? For example, imagine car buyer Blair shopping for a self-driving car, or developer Dillon trying to choose an appropriate ML model to use in their application. Their first choice might be infeasible (e.g., too expensiv…

IUI '191st AuthorExplainable AI (XAI) +2

Explaining Models: An Empirical Study of How Explanations Impact Fairness Judgment

Ensuring fairness of machine learning systems is a human-in-the-loop process. It relies on developers, users, and the general public to identify fairness problems and make improvements. To facilitate the process we need effective, unbiased, and user-friendly explanations that people can confidently rely on. Towards t…

CHI '181st AuthorExplainable AI (XAI) +2

How the Experts Do It: Assessing and Explaining Agent Behaviors in Real-Time Strategy Games

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, for if an AI-based explanation system could effectively explain intelligent agents' behavior, it could enable the end users to understand…

IUI '182nd AuthorExplainable AI (XAI) +2

Toward Foraging for Understanding of StarCraft Agents: An Empirical Study

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 this problem, but little is known about how users would forage through information an explanation system might offer. To inform the develo…

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