Last October, I reported here on the launch of an innovative project, Learned Hands, that uses a game to train a machine-learning algorithm to better identify the legal issues in the words that ordinary people use to describe their problems. The goal was to use artificial intelligence to help legal services providers better match a consumer to the appropriate attorney or legal resource.
Now, The Pew Charitable Trusts has awarded a grant to one of the sponsors of that project, Suffolk Law School’s Legal Innovation and Technology Lab, to move the project from proof of concept to production.
The grant runs through December 2020 and will be used to create issue spotters, in the forms of both an application programming interface (API) and a Python programming library, that will be free to use for public-interest groups, the LIT Lab’s director, David Colarusso, told me. It is also expected that the Legal Services Corporation will use the issue spotters in its project to develop state legal portals.
The idea behind Learned Hands was to create a game to incentivize players to crowdsource the task of spotting legal issues in real people’s stories about their legal problems. Players earn points and rankings based on how many questions they mark and the extent to which their marks are deemed correct.
The goal is to train a machine-learning algorithm to spot legal issues issues. That required both people-power to do the tagging and a collection of actual questions against which to train. For that, the project obtain a collection of some 75,000 questions posted in the Reddit forum r/legaladvice.
That project went well, Colarusso said, drawing participation by nearly 600 people who created 54,000 labels resulting in the finalization of about 2,000 of the questions. The labeling is being used to create a taxonomy
