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.<\/p>\n
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\u2019ve passed. To do so, they have to manually sift through thousands of pages of dense documentation covering global laws and victims\u2019 testimonies to find what they\u2019re looking for.<\/p>\n
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<\/a> looked to machine learning. With support from Google.org Fellows<\/a> and grant funding, they\u2019ve built new tools that can automatically tag human rights documents so they are searchable \u2014 making the curation process 13 times faster.<\/p>\n<\/div>\n<\/div>\n 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\u2019re 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\u2019re added to collections new documentation often supersedes them.<\/p>\n 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?<\/p>\n HURIDOCS took their idea to the Google AI Impact Challenge<\/a> 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.<\/p>\n<\/div>\n<\/div>\nHow machine learning can make information more accessible<\/h3>\n
![\"Google.org](\"https:\/\/img.youtube.com\/vi\/Uf3oy4Sri5g\/maxresdefault.jpg\")
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