Tag Archives: machine learning

Tell me more?: the effects of mental model soundness on personalizing an intelligent agent

ACM DL Author-ize serviceTell me more?: the effects of mental model soundness on personalizing an intelligent agent

Todd Kulesza, Simone Stumpf, Margaret Burnett, Irwin Kwan
CHI ’12 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 2012

Just yesterday at CHI2012 (ACM Conference on Computer-Human Interaction) in Austin, TX, my colleague Todd Kulesza presented our paper! Unfortunately I couldn’t be there but I’m sure that it went well. This paper was not only accepted at CHI, but it also received an honorable mention, which is absolutely spectacular.

This paper was the second project I helped with over at Oregon State University and is about how inducing a mental model in end users through training can enable them to more efficiently correct the mistakes of an intelligent agent – that is, a machine-learning system that assists users by making recommendations. The experiment that we examined was a music recommendation system. By providing instruction to these end users about the details of how these agents make decisions, the users felt that the cost-benefit ratio of making suggestions was a better use of their time and they had a more positive experience using the system overall.

Check this out, it should be appearing soon in the ACM digital library below. I’ll update it with the ACM Author-ize link when ACM provides one 🙂

Todd Kulesza, Simone Stumpf, Margaret Burnett, and Irwin Kwan. Tell Me More? The Effects of Mental Model Soundness on Personalizing an Intelligent Agent. ACM Conference on Computer-Human Interaction 2012, Austin, USA.

Update: I heard from Dr. Burnett that Todd’s talk was fantastic! I also heard that this paper is on Page 1 of the CHI 2012 proceedings.