Procedural and Structural Justice Through Causal Understanding, Component Decoupling, and Relation Characterization, 2025 (ICPSR 39655)
Principal Investigator(s): View help for Principal Investigator(s)
Zeyu Tang, Carnegie Mellon University
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Summary View help for Summary
The goal of this project is to address overlooked issues of disguised procedural violations, aiming to develop principled methods for fairness analysis. The project emphasizes achieving procedural and structural justice in criminal and juvenile justice systems, as well as broader social contexts, and had three specific aims: 1) create a fairness flowchart clarifying justice semantics, (2) develop technical approaches for procedural fairness across data types and in both static and long-run settings, and (3) develop a causality-guided framework for debiasing and evaluating language model outputs.
The data and code associated with this study are available from three repositories on GitHub:
- Algorithmic Fairness amid Social Determinants: Reflection, Characterization, and Approach
- Procedural Fairness Through Decoupling Objectionable Data Generating Components
- Tier Balancing: Towards Dynamic Fairness over Underlying Causal Factors