Collaborative Research: CIF: Small: Interpretable Fair Machine Learning: Frameworks, Robustness, and Scalable Algorithms

协作研究：CIF：小型：可解释的公平机器学习：框架、稳健性和可扩展算法

基本信息

批准号：
2246417
负责人：
Weijun Xie
金额：
$ 25万
依托单位：
Georgia Tech Research Corporation
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-10-01 至 2025-03-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2246417&HistoricalAwards=false
关键词：
Collaborative Research CIF Small Interpretable

项目摘要

Machine-learning algorithms are revolutionizing modern decision-making processes, from deciding job offers, evaluating loans, and determining university enrollments to proposing medical interventions. However, despite the recent success of machine-learning algorithms in solving large-scale problems, serious concerns have been raised that they are not entirely objective and can inadvertently amplify human biases. The proposed research project addresses this fundamental shortcoming by developing scalable data-driven methods and algorithms that generate interpretable policies aiming for provable fairness guarantees. The project will inform the policy-makers or decision-makers about possible outcomes and tradeoffs between machine learning outcomes and social equity/fairness. Furthermore, the research results will provide guidelines to support policies as well as regulations to promote diversity and fairness in many relevant domains of application. The proposed research leverages recent advances in discrete and robust optimization, aiming for solution methodologies that faithfully address the exact learning models with fairness measures, provide strong out-of-sample fairness guarantees, are robust against bias and noisy outliers in the dataset, and can be solved efficiently for large-scale problem instances. More specifically, the proposed research aims to develop effective new frameworks for fair learning via sub-data selection that can leverage past efforts and enhance the fairness in the learning outcomes. Robust solution schemes will be carefully designed to significantly mitigate the severe overfitting effects of empirical-based methods and improve out-of-sample performance. Efforts will also be devoted to addressing algorithmic fairness in multi-stage decision-making and resource-allocation problems.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.

机器学习算法正在彻底改变现代决策过程，从决定工作，评估贷款和确定大学入学率到提出医疗干预措施。但是，尽管机器学习算法最近在解决大规模问题方面取得了成功，但人们严重担心它们并不完全客观，并且会无意中扩大人类偏见。拟议的研究项目通过开发可扩展的数据驱动方法和算法来解决这一基本缺点，这些方法和算法生成了可解释的政策，以实现可证明的公平保证。该项目将向政策制定者或决策者提供有关机器学习成果与社会公平/公平性之间可能的成果和权衡的信息。此外，研究结果将为支持政策和法规提供指南，以促进许多相关应用领域的多样性和公平性。拟议的研究利用了离散和强大优化的最新进展，目的是实现解决方案方法，以公平性措施忠实地解决精确的学习模型，提供强大的样本外公平保证，对数据集中的偏见和嘈杂的异常值具有强大的态度，并且可以有效地解决大规模问题的情况。更具体地说，拟议中的研究旨在通过选项的选择开发有效的新框架，以利用过去的努力并增强学习成果的公平性。强大的解决方案方案将经过仔细设计，以显着减轻基于经验的方法的严重过度拟合效应并改善样本外的性能。努力还将致力于解决多阶段决策和资源分配问题中的算法公平性。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛影响的评估评估标准来通过评估来获得支持的。