Learned Hands is a game in which you spot possible legal issues in real people’s stories about their problems. You read the stories, and then say whether you see a certain legal issue — family law issues, consumer law issues, criminal law issues, etc.
The game is also a research project. Each time you play, you are training a machine learning model to be able to spot people’s legal issues. This model will be used to develop access to justice technologies that connect people with public legal help resources. It will help us to make a Rosetta Stone for legal help — linking the legal help guides that courts and legal aid groups offer to the people who are searching for help.
Learned Hands is a project of Stanford Legal Design Lab and Suffolk's Legal Innovation & Technology (LIT) Lab. It was created with support from The Pew Charitable Trusts. It is a tool for lawyers, law students, and others with legal expertise to spot legal issues in a piece of text. It helps us to train machine learning models on how to identify legal issues.
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