Interfacing thought: cognitive aspects of human-computer interaction
ACM Transactions on Computer-Human Interaction (TOCHI)
Debugging and the experience of immediacy
Communications of the ACM
Getting more out of programming-by-demonstration
Proceedings of the SIGCHI conference on Human Factors in Computing Systems
Automatic Indexing: An Experimental Inquiry
Journal of the ACM (JACM)
Visualizing the simple Baysian classifier
Information visualization in data mining and knowledge discovery
A model of textual affect sensing using real-world knowledge
Proceedings of the 8th international conference on Intelligent user interfaces
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
End-user software engineering with assertions in the spreadsheet paradigm
Proceedings of the 25th International Conference on Software Engineering
An introduction to variable and feature selection
The Journal of Machine Learning Research
Supporting user hypotheses in problem diagnosis
Proceedings of the 9th international conference on Intelligent user interfaces
A review of explanation methods for Bayesian networks
The Knowledge Engineering Review
Six Learning Barriers in End-User Programming Systems
VLHCC '04 Proceedings of the 2004 IEEE Symposium on Visual Languages - Human Centric Computing
Sensitivity analysis in Bayesian networks: from single to multiple parameters
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Effectiveness of end-user debugging software features: are there gender issues?
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Answering why and why not questions in user interfaces
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Supporting end-user debugging: what do users want to know?
Proceedings of the working conference on Advanced visual interfaces
Toward harnessing user feedback for machine learning
Proceedings of the 12th 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
Koala: capture, share, automate, personalize business processes on the web
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Explaining Debugging Strategies to End-User Programmers
VLHCC '07 Proceedings of the IEEE Symposium on Visual Languages and Human-Centric Computing
Testing vs. code inspection vs. what else?: male and female end users' debugging strategies
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Investigating statistical machine learning as a tool for software development
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Integrating rich user feedback into intelligent user interfaces
Proceedings of the 13th international conference on Intelligent user interfaces
Recovering from errors during programming by demonstration
Proceedings of the 13th international conference on Intelligent user interfaces
Toward establishing trust in adaptive agents
Proceedings of the 13th 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
Can feature design reduce the gender gap in end-user software development environments?
VLHCC '08 Proceedings of the 2008 IEEE Symposium on Visual Languages and Human-Centric Computing
Interacting meaningfully with machine learning systems: Three experiments
International Journal of Human-Computer Studies
Visual explanation of evidence in additive classifiers
IAAI'06 Proceedings of the 18th conference on Innovative applications of artificial intelligence - Volume 2
Assessing demand for intelligibility in context-aware applications
Proceedings of the 11th international conference on Ubiquitous computing
When do numbers really matter?
Journal of Artificial Intelligence Research
Interactive optimization for steering machine classification
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
End-user feature labeling: a locally-weighted regression approach
Proceedings of the 16th international conference on Intelligent user interfaces
An explanation-centric approach for personalizing intelligent agents
Proceedings of the 2012 ACM international conference on Intelligent User Interfaces
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Machine learning techniques are increasingly used in intelligent assistants, that is, software targeted at and continuously adapting to assist end users with email, shopping, and other tasks. Examples include desktop SPAM filters, recommender systems, and handwriting recognition. Fixing such intelligent assistants when they learn incorrect behavior, however, has received only limited attention. To directly support end-user “debugging” of assistant behaviors learned via statistical machine learning, we present a Why-oriented approach which allows users to ask questions about how the assistant made its predictions, provides answers to these “why” questions, and allows users to interactively change these answers to debug the assistant's current and future predictions. To understand the strengths and weaknesses of this approach, we then conducted an exploratory study to investigate barriers that participants could encounter when debugging an intelligent assistant using our approach, and the information those participants requested to overcome these barriers. To help ensure the inclusiveness of our approach, we also explored how gender differences played a role in understanding barriers and information needs. We then used these results to consider opportunities for Why-oriented approaches to address user barriers and information needs.