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Fixing the program my computer learned: barriers for end users, challenges for the machine
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International Conference on Intelligent User Interfaces archive
Proceedings of the 13th international conference on Intelligent user interfaces table of contents
Sanibel Island, Florida, USA
SESSION: Demonstration based interfaces table of contents
Pages 187-196  
Year of Publication: 2009
ISBN:978-1-60558-168-2
Authors
Todd Kulesza  Oregon State University, Corvallis, OR, USA
Weng-Keen Wong  Oregon State University, Corvallis, OR, USA
Simone Stumpf  Oregon State University, Corvallis, OR, USA
Stephen Perona  Oregon State University, Corvallis, OR, USA
Rachel White  Oregon State University, Corvallis, OR, USA
Margaret M. Burnett  Oregon State University, Corvallis, OR, USA
Ian Oberst  Oregon State University, Corvallis, OR, USA
Andrew J. Ko  University of Washington, Seattle, WA, USA
Sponsors
SIGCHI: ACM Special Interest Group on Computer-Human Interaction
ACM: Association for Computing Machinery
SIGART: ACM Special Interest Group on Artificial Intelligence
Publisher
ACM  New York, NY, USA
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ABSTRACT

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.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Collaborative Colleagues:
Todd Kulesza: colleagues
Weng-Keen Wong: colleagues
Simone Stumpf: colleagues
Stephen Perona: colleagues
Rachel White: colleagues
Margaret M. Burnett: colleagues
Ian Oberst: colleagues
Andrew J. Ko: colleagues