Disentangling Societal Inequality from Model Biases: Gender Inequality
  in Divorce Court Proceedings
Sujan Dutta Parth Srivastava Vaishnavi Solunke Swaprava Nath Ashiqur R. KhudaBukhsh
Divorce is the legal dissolution of a marriage by a court. Since this is
usually an unpleasant outcome of a marital union, each party may have reasons
to call the decision to quit which is generally documented in detail in the
court proceedings. Via a substantial corpus of 17,306 court proceedings, this
paper investigates gender inequality through the lens of divorce court
proceedings. While emerging data sources (e.g., public court records) on
sensitive societal issues hold promise in aiding social science research,
biases present in cutting-edge natural language processing (NLP) methods may
interfere with or affect such studies. We thus require a thorough analysis of
potential gaps and limitations present in extant NLP resources. In this paper,
on the methodological side, we demonstrate that existing NLP resources required
several non-trivial modifications to quantify societal inequalities. On the
substantive side, we find that while a large number of court cases perhaps
suggest changing norms in India where women are increasingly challenging
patriarchy, AI-powered analyses of these court proceedings indicate striking
gender inequality with women often subjected to domestic violence.