How machine learning can make information more accessible

Typically, non-governmental organizations collect and curate large bodies of human rights information, with the goal of making these collections useful for advocates. Manually processing these documents can take several days, particularly when they’re published in unfamiliar languages or in PDF format which is difficult to search through. As a result, many NGOs face a large backlog of documents that remain to be processed, and by the time they’re added to collections new documentation often supersedes them.

Based in Geneva, HURIDOCS has been developing tools to manage and analyze collections of human rights evidence, law and research for nearly four decades. In 2016, they had an idea: What if machine learning could skim through documents, make terms extractable, and classify the content to catalog documents more quickly?

HURIDOCS took their idea to the Google AI Impact Challenge and was selected for a $1 million grant from Google.org and six months of technical support from a team of seven full-time pro bono Google.org Fellows. As one of the Fellows, I helped train AI models and make sure that the tool was useful to human rights experts, not just machine learning experts.

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