Authors: Razieh Nabi, Nima S. Hejazi, Mark J. van der Laan, David Benkeser

Published on: April 15, 2024

Impact Score: 8.0

Arxiv code: Arxiv:2404.09847

Summary

• What is new: The research presents a new framework for achieving fairness in machine learning models through a constrained learning approach, providing closed-form solutions for optimal parameters.
• Why this is important: The challenge of developing predictive models that adhere to pre-defined fairness constraints without sacrificing predictive accuracy.
• What the research proposes: A Lagrange multiplier formulation for constrained statistical machine learning, allowing for the identification of function-valued parameters that meet fairness constraints.
• Results: Demonstration that closed-form solutions for optimal constrained parameters can lead to fairer predictive models, applicable across various statistical learning approaches.

Technical Details

Technological frameworks used: Lagrange multiplier formulation for penalized risk criterion

Models used: Statistical machine learning models with fairness constraints

Data used: Estimates from unconstrained parameters of the data generating distribution

Potential Impact

Companies and sectors relying on predictive algorithms for decision-making, including finance, healthcare, and HR, could significantly benefit or be disrupted by the adoption of these fairness-enforcing methods.

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