Paper Info
Title
Integrating rich user feedback into intelligent user interfaces
Abstract
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.
Year
DOI
Venue
2008
10.1145/1378773.1378781
IUI
Keywords
Field
DocType
classifier behavior,initial training phase,real-time keyword-based feedback,improved accuracy,generous amount,beneficial exchange,clear explanation,rich feedback,initial unstable period,intelligent user interface,rich user feedback,graceful manner,real time,technical report,human factors,machine learning
Assimilation (phonology),Active learning (machine learning),Computer science,Human–computer interaction,Artificial intelligence,Error-driven learning,User interface,Classifier (linguistics),Multimedia,Machine learning
Conference
Citations 
PageRank 
References 
19
0.88
19
Authors
6
Name
Order
Citations
PageRank
Simone Stumpf146832.52
Erin Sullivan21134.84
Erin Fitzhenry3331.60
Ian Oberst41236.22
Weng-Keen Wong581759.67
Margaret M. Burnett63607262.34