An Empirical Comparison of Bias Reduction Methods on Real-World Problems in High-Stakes Policy Settings

by   Hemank Lamba, et al.

Applications of machine learning (ML) to high-stakes policy settings – such as education, criminal justice, healthcare, and social service delivery – have grown rapidly in recent years, sparking important conversations about how to ensure fair outcomes from these systems. The machine learning research community has responded to this challenge with a wide array of proposed fairness-enhancing strategies for ML models, but despite the large number of methods that have been developed, little empirical work exists evaluating these methods in real-world settings. Here, we seek to fill this research gap by investigating the performance of several methods that operate at different points in the ML pipeline across four real-world public policy and social good problems. Across these problems, we find a wide degree of variability and inconsistency in the ability of many of these methods to improve model fairness, but post-processing by choosing group-specific score thresholds consistently removes disparities, with important implications for both the ML research community and practitioners deploying machine learning to inform consequential policy decisions.


Machine learning for public policy: Do we need to sacrifice accuracy to make models fair?

Growing applications of machine learning in policy settings have raised ...

Does the End Justify the Means? On the Moral Justification of Fairness-Aware Machine Learning

Despite an abundance of fairness-aware machine learning (fair-ml) algori...

A Human-Centric Take on Model Monitoring

Predictive models are increasingly used to make various consequential de...

FairGBM: Gradient Boosting with Fairness Constraints

Machine Learning (ML) algorithms based on gradient boosted decision tree...

How Personal is Machine Learning Personalization?

Though used extensively, the concept and process of machine learning (ML...

Explainable Machine Learning for Public Policy: Use Cases, Gaps, and Research Directions

In Machine Learning (ML) models used for supporting decisions in high-st...

Fair Data Integration

The use of machine learning (ML) in high-stakes societal decisions has e...