EAGER: Towards Fair Regression under Sample Selection Bias

决策模型在就业、信贷和保险等应用中无处不在。越来越多的人担心，基于数据集训练的预测决策模型会产生不准确的决策，甚至是歧视。公平的机器学习已经成为一个越来越重要的话题。公平机器学习模型旨在学习目标变量的函数，同时确保基于给定公平性标准的预测值是公平的。现有的大部分工作都集中在公平分类上。本项目研究贷款金额等决策是连续的公平回归，重点研究用于构建模型的现有数据与模型未来数据的分布不同的场景。特别是，这个项目处理样本选择偏差，其中训练数据集中的因变量的值缺失。该项目旨在通过偏差校正和优化技术，为建立的回归模型提供一个统一的框架和实用的解决方案，以实现严格的公平性和高精度。这个项目的技术目标分为三个重点。第一部分发展了样本选择偏差下公平回归的统一框架。该框架采用经典的Heckman模型来纠正偏见，并通过约束优化来强制执行多个高级公平概念。第二个推力应用拉格朗日对偶理论并发展约简方法来解决约束优化问题。本文将对实现公平概念的强对偶性和推导有效优化的近似技术进行理论研究。第三个重点是通过基准数据集和实际应用对开发的框架和算法在预测准确性和公平性方面进行实证评估，并将算法实现并集成到开源库中，以实现公平的机器学习。研究结果有望促进对公平回归的理论认识，提高其在处理样本选择偏差方面的适用性，并有助于公平回归算法在实际系统中的应用。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Decision making models are ubiquitous in applications like employment, credit, and insurance. Increasingly, there are worries of inaccurate decisions or even discrimination from predictive decision models that have been trained on a collection of data. Fair machine learning has been an increasingly important topic. Fair machine learning models aim to learn a function for a target variable while ensuring the predicted value is fair based on a given fairness criterion. Much of the existing work focuses on fair classification. This project researches fair regression where the decision such as loan amount is continuous and focuses on the scenario where the existing data for building the model have different distributions from the model's future data. In particular, this project deals with the sample selection bias where the values for the dependent variable from the training dataset are missing. The project aims to develop a unified framework and practical solutions for achieving rigorous fairness and high accuracy of the built regression model via bias correction and optimization techniques. The technical aims of this project are divided into three thrusts. The first thrust develops the unified framework for fair regression under sample selection bias. The framework adopts the classic Heckman model to correct bias and enforces multiple advanced fairness notions via constrained optimization. The second thrust applies the Lagrange duality theory and develops reduction approaches to solve constrained optimization. Theoretical studies of achieving strong duality for fairness notions and research of deriving approximation techniques for efficient optimization will be conducted in this thrust. The third thrust conducts empirical evaluation of the developed framework and algorithms in terms of prediction accuracy and fairness with benchmark datasets and real applications, implements and integrates the algorithms into open source libraries for fair machine learning. The research findings expect to advance theoretical understanding of fair regression, improve its applicability for handling sample selection bias, and help transition of fair regression algorithms to use in real systems.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.