FairSense: Long-Term Fairness Analysis of ML-Enabled Systems
This addresses fairness issues in dynamic ML systems for stakeholders like policymakers and developers, though it is incremental as it builds on existing fairness methods by focusing on long-term effects.
The authors tackled the problem of long-term fairness violations in ML-enabled systems due to self-reinforcing feedback loops, proposing FairSense, a simulation-based framework that detects and analyzes such unfairness through Monte-Carlo simulation and sensitivity analysis, demonstrated in three real-world case studies.
Algorithmic fairness of machine learning (ML) models has raised significant concern in the recent years. Many testing, verification, and bias mitigation techniques have been proposed to identify and reduce fairness issues in ML models. The existing methods are model-centric and designed to detect fairness issues under static settings. However, many ML-enabled systems operate in a dynamic environment where the predictive decisions made by the system impact the environment, which in turn affects future decision-making. Such a self-reinforcing feedback loop can cause fairness violations in the long term, even if the immediate outcomes are fair. In this paper, we propose a simulation-based framework called FairSense to detect and analyze long-term unfairness in ML-enabled systems. Given a fairness requirement, FairSense performs Monte-Carlo simulation to enumerate evolution traces for each system configuration. Then, FairSense performs sensitivity analysis on the space of possible configurations to understand the impact of design options and environmental factors on the long-term fairness of the system. We demonstrate FairSense's potential utility through three real-world case studies: Loan lending, opioids risk scoring, and predictive policing.