NSF-NSERC: Fairness Fundamentals: Geometry-inspired Algorithms and Long-term Implications

NSF-NSERC：公平基础：几何启发的算法和长期影响

• 批准号: 2342253
• 负责人: Ramtin Pedarsani
• 金额: $ 44万
• 依托单位: University of California-Santa Barbara
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-04-01 至 2027-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2342253&HistoricalAwards=false
• 关键词: NSF; NSERC; Fairness; Fundamentals; Geometry

It is well-known that machine learning algorithms can show bias in decision making towards certain individuals or groups with sensitive attributes such as gender and ethnicity. This bias can be due to several reasons including biases in data, algorithmic biases, and biases in human interpretation. To mitigate this effect, one needs to better understand the learning procedure, especially when popular deep learning models are employed in order to aid decisionmakers in making fair machine decisions. Moreover, in sequential decision making setups, it is important to consider the long-term impact of automated decisions on fairness. This proposal aims to tackle the fairness challenge in machine learning by focusing on the effect of different biases on a deep learning model, as well as being cognizant of the long-term effects of fairness. The project engages undergraduate students in research and has several outreach activities involving pre-college students.The goal of this project is to develop theoretical frameworks for studying how data biases impact the fairness of learning-based decision-making algorithms in both static and dynamic settings. First, the effect of data imbalances on the geometry of models learned by deep neural networks when various training loss functions are utilized will be investigated. Building on the theoretical framework, principled algorithms will be designed that provably enhance fairness in decision making. Finally, models will be developed that tackle the challenge of guaranteeing fairness in dynamic settings where group imbalance and feature distributions evolve over time depending on the decisions. While the project focus is on theoretical understanding and formalizing fairness concepts in machine learning, various numerical evaluations will be conducted to implement the developed algorithms throughout the duration of the project.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.

众所周知，机器学习算法可能会在决策中对某些具有敏感属性（如性别和种族）的个人或群体表现出偏见。这种偏差可能是由于几个原因，包括数据偏差，算法偏差和人类解释的偏差。为了减轻这种影响，人们需要更好地理解学习过程，特别是当使用流行的深度学习模型来帮助决策者做出公平的机器决策时。此外，在顺序决策设置中，重要的是要考虑自动化决策对公平性的长期影响。该提案旨在通过关注不同偏见对深度学习模型的影响，以及认识到公平性的长期影响，来解决机器学习中的公平性挑战。该项目吸引本科生参与研究，并有几个涉及大学预科生的推广活动。该项目的目标是开发理论框架，研究数据偏差如何影响静态和动态环境中基于学习的决策算法的公平性。首先，将研究当使用各种训练损失函数时，数据不平衡对深度神经网络学习的模型几何结构的影响。在理论框架的基础上，将设计原则性算法，可证明提高决策的公平性。最后，将开发模型，以解决在动态环境中保证公平性的挑战，在动态环境中，组不平衡和特征分布随着时间的推移而变化，这取决于决策。虽然该项目的重点是理论理解和形式化的机器学习的公平概念，各种数值评估将进行实施开发的算法在整个项目的持续时间。这个奖项反映了NSF的法定使命，并已被认为是值得支持的评估使用基金会的智力价值和更广泛的影响审查标准。