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Juan-Pablo Velez edited this page Nov 17, 2013 · 27 revisions

<img src="https://raw.github.com/wiki/dssg/ushine-learning/pic/logo_ushahidi.png" width="400px"/ align="right">

Ushine Learning is a machine learning API built to support Ushahidi, a crowdsourced crisis reporting platform. During crisis situations - from natural disasters to contested elections - people can use the Ushahidi software to gather reports from the field and publish those reports to the world in maps, lists, and more.

Currently, as volunteers submit reports to the Ushahidi platform, each report has to be reviewed and labeled by a team of humans in a highly manual process. Ushine learning seeks to make that annotation process more efficient, by using machine learning to detect a report's language, location, category, potentially sensitive information, and near-duplicate messages.

But we didn't just build an API to perform this detection. We also sought to demonstrate that using this API to provide automatic suggestions to human annotators could make them faster and more accurate as they reviewed reports. In other words, we measured the API's performance. To do this, we ran multiple evaluations: a quantitative analysis of our API performance, a quantitative user experiment to track the impact of machine suggestions on human annotation accuracy and speed, and a qualitative survey on user satisfaction. Our results suggest that we can improve both speed and accuracy by providing automatic suggestions.

This wiki is the central place to learn about the problem we're solving, the data we worked with, the machine learning techniques we used, the API we built, and the evaluations we performed on the API.

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