PC-Fairness: A Unified Framework for Measuring Causality-based Fairness
Yongkai Wu · Lu Zhang · Xintao Wu · Hanghang Tong

Thu Dec 12th 10:45 AM -- 12:45 PM @ East Exhibition Hall B + C #84

A recent trend of fair machine learning is to define fairness as causality-based notions which concern the causal connection between protected attributes and decisions. However, one common challenge of all causality-based fairness notions is identifiability, i.e., whether they can be uniquely measured from observational data, which is a critical barrier to applying these notions to real-world situations. In this paper, we develop a framework for measuring different causality-based fairness. We propose a unified definition that covers most of previous causality-based fairness notions, namely the path-specific counterfactual fairness (PC fairness). Based on that, we propose a general method in the form of a constrained optimization problem for bounding the path-specific counterfactual fairness under all unidentifiable situations. Experiments on synthetic and real-world datasets show the correctness and effectiveness of our method.

Author Information

Yongkai Wu (University of Arkansas)

Yongkai Wu is a fifth-year Ph.D. student at the University of Arkansas, advised by [Xintao Wu]( His research interests include machine learning and causal inference, especially fairness-aware machine learning. **He is expected to graduate in May 2020 and actively seeking for an assistant professor or research scientist position.**

Lu Zhang (University of Arkansas)
Xintao Wu (University of Arkansas)

Dr. Xintao Wu is the professor and the Charles D. Morgan/Acxiom Endowed Graduate Research Chair in Database and leads Social Awareness and Intelligent Learning (SAIL) Lab in Computer Science and Computer Engineering Department at University of Arkansas.

Hanghang Tong (Arizona State University)