Machine Learning Classification of VC Contract Terms
, Assistant Professor and David G. Booth Faculty Fellow
We seek to create new data on VC contracting, especially around the emerging area of impact investing. We plan to create a public facing website where VCs and entrepreneurs can assess the relative strengths of their contracts, across multiple dimensions, by uploading legal documents. Building off pilot data and a process we have already developed, we will leverage natural language processing and maching learning algorithms to evaluate the contract terms supplied. Participants will receive a contract score benchmarked against comparable documents and an opportunity to provide feedback on the scoring application. Researchers will receive three benefits from the process: (1) access to otherwide hard to obtain data about private market investments, (2) improvement of the research process through automation and incorporating feedback, and (3) an audience for market research from a growing repository of VC investors and entrepreneurs.