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Erin Sullivan
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Publications by Erin Sullivan (bibliography)
» 2008 «
Stumpf, Simone, Sullivan, Erin, Fitzhenry, Erin, Oberst, Ian, Wong, Weng-Keen and Burnett, Margaret (2008): Integrating rich user feedback into intelligent user interfaces. In: Proceedings of the 2008 International Conference on Intelligent User Interfaces 2008. pp. 50-59. Available online
The potential for machine learning systems to improve via a mutually beneficial exchange of information with users has yet to be explored in much detail. Previously, we found that users were willing to provide a generous amount of rich feedback to machine learning systems, and that the types of some of this rich feedback seem promising for assimilation by machine learning algorithms. Following up on those findings, we ran an experiment to assess the viability of incorporating real-time keyword-based feedback in initial training phases when data is limited. We found that rich feedback improved accuracy but an initial unstable period often caused large fluctuations in classifier behavior. Participants were able to give feedback by relying heavily on system communication in order to respond to changes. The results show that in order to benefit from the user's knowledge, machine learning systems must be able to absorb keyword-based rich feedback in a graceful manner and provide clear explanations of their predictions.
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» 2007 «
Stumpf, Simone, Rajaram, Vidya, Li, Lida, Burnett, Margaret, Dietterich, Thomas G., Sullivan, Erin, Drummond, Russell and Herlocker, Jonathan (2007): Toward harnessing user feedback for machine learning. In: Proceedings of the 2007 International Conference on Intelligent User Interfaces 2007. pp. 82-91. Available online
There has been little research into how end users might be able to communicate advice to machine learning systems. If this resource -- the users themselves -- could somehow work hand-in-hand with machine learning systems, the accuracy of learning systems could be improved and the users' understanding and trust of the system could improve as well. We conducted a think-aloud study to see how willing users were to provide feedback and to understand what kinds of feedback users could give. Users were shown explanations of machine learning predictions and asked to provide feedback to improve the predictions. We found that users had no difficulty providing generous amounts of feedback. The kinds of feedback ranged from suggestions for reweighting of features to proposals for new features, feature combinations, relational features, and wholesale changes to the learning algorithm. The results show that user feedback has the potential to significantly improve machine learning systems, but that learning algorithms need to be extended in several ways to be able to assimilate this feedback.
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Mar 21st, 2010
Changes to this page (author)
24 Feb 2010: Enabled abstracts to be shown on Erin Sullivan's author page.08 Apr 2009: Author was edited 24 Jul 2007: Author was added to the bibliography
Publication statistics
Publication period:2007-2008
Publication count:2
Number of co-authors:10
Productive colleagues
Erin Sullivan's 3 most productive colleagues in number of publications:
Margaret Burnett:20Thomas G. Dietterich:6Simone Stumpf:5Collaboration count
Number of publications with 3 favourite co-authors:
Margaret Burnett:2Simone Stumpf:2Erin Fitzhenry:1
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Software design is the act of determining the user's experience with a piece of software. It has nothing to do with how the code works inside, or how big or small the code is. The designer's task is to specify completely and unambiguously the user's whole experience.
-- David Liddle, From Bringing Design to Software, edited by Terry Winograd, 1996
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