Paper reading #21: Towards maximizing the accuracy of human-labeled sensor data

Comments

Reference InformationTitle: Towards maximizing the accuracy of human-labeled sensor data
Authors: Stephanie L. Rosenthal and Anind K. Dey
Presentation Venue: IUI 2010: Proceedings of the 15th international conference on Intelligent user interfaces; February 7-10, 2010; Hong Kong, China

Summary

In this paper, the authors discuss the impact that different amounts of information have on people when they label things. They discuss 5 main types of information given to labelers such a: Different amounts of contextual information, High and low level explanations, Prediction, User Feedback, and Level of uncertainty.

A study was conducted by the authors where they used the wizard-of-oz technique and presented labelers with varying amounts of information to test their labeling accuracy. They also focused on the differences between people labeling data they had not seen before and their own data.

After the study, they found that the five types of information had a positive affect on the labelers, because it gave them more information or helped to direct their thought processes.

Finally, the researchers found that whether the labeler was familiar with the had no impact on the accuracy of the label.

Discussion
The was one of those papers where I feel, if you do not have some type of background about the subject, you will be left in the dark. This paper was hard to follow, in my opinion. Nonetheless, the idea of labeling and the accuracy to which people do so was interesting. Either than that, I don't quite get the usefulness of this study.  I didn't understand why they were doing it as they did not have a good explanation in any part of the paper.