Disentangling Societal Inequality from Model Biases: Gender Inequality in Divorce Court Proceedings

TL;DR

This study uses a large corpus of divorce court proceedings to analyze gender inequality, revealing persistent disparities and biases in legal outcomes and societal norms, while also addressing limitations of NLP tools in such analyses.

Contribution

The paper demonstrates necessary modifications to NLP resources for analyzing societal inequalities and provides empirical evidence of gender disparities in Indian divorce court cases.

Findings

Women face significant domestic violence issues in court records.

NLP tools require adaptations to accurately analyze societal biases.

Court proceedings reflect ongoing gender inequality despite changing norms.

Abstract

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.