In America today fewer than one in five low-income individuals have access to the legal help they need.
Many individuals become self-represented litigants (SRLs) and achieve poor outcomes as a result.
Federal spending on legal aid would need to increase from $300M to $1.6B to resolve these issues.
The goal of this project is to identify innovative algorithmic and analytic methods for meeting these needs without increasing federal spending.
Specifically, we aim to provide helpful and personalized assistance to SRLs via optimized matching of SRLs with attorneys and courts.