Mental models: towards a cognitive science of language, inference, and consciousness
Mental models: towards a cognitive science of language, inference, and consciousness
Smalltalk scaffolding: a case study of minimalist instruction
CHI '90 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
The role of trust in automation reliance
International Journal of Human-Computer Studies - Special issue: Trust and technology
Improving proactive information systems
Proceedings of the 10th international conference on Intelligent user interfaces
How it works: a field study of non-technical users interacting with an intelligent system
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Toward establishing trust in adaptive agents
Proceedings of the 13th international conference on Intelligent user interfaces
Tagsplanations: explaining recommendations using tags
Proceedings of the 14th international conference on Intelligent user interfaces
Fixing the program my computer learned: barriers for end users, challenges for the machine
Proceedings of the 14th international conference on Intelligent user interfaces
EnsembleMatrix: interactive visualization to support machine learning with multiple classifiers
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Why and why not explanations improve the intelligibility of context-aware intelligent systems
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Interacting meaningfully with machine learning systems: Three experiments
International Journal of Human-Computer Studies
Interactive optimization for steering machine classification
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Examining multiple potential models in end-user interactive concept learning
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Toolkit to support intelligibility in context-aware applications
Proceedings of the 12th ACM international conference on Ubiquitous computing
Explanatory Debugging: Supporting End-User Debugging of Machine-Learned Programs
VLHCC '10 Proceedings of the 2010 IEEE Symposium on Visual Languages and Human-Centric Computing
Where are my intelligent assistant's mistakes? a systematic testing approach
IS-EUD'11 Proceedings of the Third international conference on End-user development
Why-oriented end-user debugging of naive Bayes text classification
ACM Transactions on Interactive Intelligent Systems (TiiS)
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Intelligent agents are becoming ubiquitous in the lives of users, but the research community has only recently begun to study how people establish trust in and communicate with such agents. I plan to design an explanation-centric approach to support end users in personalizing their intelligent agents and in assessing their strengths and weaknesses. My goal is to define an approach that helps people understand when they can rely on their intelligent agents' decisions, and allows them to directly debug their agents' reasoning when it does not align with their own.