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。越来越多的人担心决策不准确，甚至是在数据集合上训练的预测决策模型的歧视。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，同时确保预测值基于给定的公平标准是公平的。 现有的大部分工作都集中在公平分类上。本项目研究贷款额等决策连续的公平回归，并关注用于构建模型的现有数据与模型的未来数据具有不同分布的情况。 特别是，该项目处理样本选择偏差，其中训练数据集中因变量的值缺失。 该项目旨在开发一个统一的框架和实用的解决方案，通过偏差校正和优化技术实现所建回归模型的严格公平性和高准确性。 该项目的技术目标分为三个方面。第一个推力发展的统一框架下的样本选择偏差的公平回归。该框架采用经典的Heckman模型来纠正偏差，并通过约束优化来实施多种先进的公平性概念。第二个推力应用拉格朗日对偶理论和发展减少的方法来解决约束优化。实现强对偶的公平性概念的理论研究和推导出有效的优化近似技术的研究将在这一推力进行。第三个重点是利用基准数据集和真实的应用程序对所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，实现并将算法集成到开源库中以实现公平的机器学习。 该研究成果有望推进对公平回归的理论理解，提高其处理样本选择偏差的适用性，并帮助公平回归算法过渡到真实的system.This奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

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.