Toward harnessing user feedback for machine learning
Toward harnessing user feedback for machine learning
复制标题
利用用户反馈进行机器学习
DOI：
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发表时间：
2007
期刊：
影响因子：
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通讯作者：
Jonathan L. Herlocker
中科院分区：
文献类型：
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作者：
S. Stumpf;Vidya Rajaram;Lida Li;M. Burnett;Thomas G. Dietterich;Erin Sullivan;Russell Drummond;Jonathan L. Herlocker
关于最终用户如何能够向机器学习系统传达建议的研究很少。如果这种资源-用户本身-可以与机器学习系统携手合作，学习系统的准确性可以提高，用户对系统的理解和信任也可以提高。我们进行了一项有声思维研究，以了解用户提供反馈的意愿，并了解用户可以提供什么样的反馈。向用户展示了机器学习预测的解释，并要求他们提供反馈以改进预测。我们发现用户在提供大量反馈方面没有任何困难。反馈的种类从对特征重新加权的建议到对新特征、特征组合、关系特征的建议，以及对学习算法的大规模更改。结果表明，用户反馈有可能显着改善机器学习系统，但学习算法需要在几个方面进行扩展，以便能够吸收这种反馈。
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.