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Unlocking human rights information with machine learning

Google.org Fellow Grace Kwak Danciu sits next to Friedhelm Weinberg, Executive Director of HURIDOCS, while looking at a computer while they work on machine learning models to automatically catalog human rights documents.

Human rights defenders need information from many sources to do their work effectively. But as issues evolve and new precedents are set, finding the right information to defend a particular case can be like looking for a needle in a haystack.

For example, a human rights advocate campaigning for LGBTQ rights may want to know which countries have made the most progress and what resolutions they’ve passed. To do so, they have to manually sift through thousands of pages of dense documentation covering global laws and victims’ testimonies to find what they’re looking for.

The curation and cataloging of documents makes this process much easier, but still relies on the manual work of skilled experts. To help, the non-profit organization HURIDOCS looked to machine learning. With support from Google.org Fellows and grant funding, they’ve built new tools that can automatically tag human rights documents so they are searchable — making the curation process 13 times faster.

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.

The curation process of human rights documents gets a boost

Since then, HURIDOCS has launched ML-powered features to improve platforms they’ve built with other NGO partners, and, earlier this year, they began integrating the technology into more of its tools, including their flagship application Uwazi. As a result, updating documents now takes one week instead of two to three months, and curators have been able to catch up on multi-year document backlogs.

In June, HURIDOCS won a CogX Award for its machine learning work, and now the organization is continuing to explore what else its machine learning models can do — from creating automatic tables of contents for documents to identifying references within text. With the power of artificial intelligence, HURIDOCS hopes to solve the trickiest challenges facing human rights defenders.

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