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Weng-Keen Wong

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Publications by Weng-Keen Wong (bibliography)

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Kulesza, Todd, Wong, Weng-Keen, Stumpf, Simone, Perona, Stephen, White, Rachel, Burnett, Margaret M., Oberst, Ian and Ko, Andrew J. (2009): Fixing the program my computer learned: barriers for end users, challenges for the machine. In: Proceedings of the 2009 International Conference on Intelligent User Interfaces 2009. pp. 187-196. Available online

The results of a machine learning from user behavior can be thought of as a program, and like all programs, it may need to be debugged. Providing ways for the user to debug it matters, because without the ability to fix errors users may find that the learned program's errors are too damaging for them to be able to trust such programs. We present a new approach to enable end users to debug a learned program. We then use an early prototype of our new approach to conduct a formative study to determine where and when debugging issues arise, both in general and also separately for males and females. The results suggest opportunities to make machine-learned programs more effective tools.

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» 2008 «

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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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Changes to this page (author)

10 Feb 2010: Enabled abstracts to be shown on Weng-Keen Wong's author page.
02 Jun 2009: Author was edited
08 Apr 2009: Author was added to the bibliography

Publication statistics

Publication period:2008-2009
Publication count:2
Number of co-authors:10



Productive colleagues

Weng-Keen Wong's 3 most productive colleagues in number of publications:

Margaret M. Burnett:48
Margaret Burnett:20
Andrew J. Ko:13


Collaboration count

Number of publications with 3 favourite co-authors:

Ian Oberst:2
Simone Stumpf:2
Rachel White:1

 

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Learn more about Weng-Keen Wong:
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Mar 21

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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