CRII: CIF: Information Theoretic Measures for Fairness-aware Supervised Learning

CRII：CIF：公平意识监督学习的信息论措施

• 批准号: 2246058
• 负责人: Mohamed Nafea
• 金额: $ 17.49万
• 依托单位: University of Detroit Mercy
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2246058&HistoricalAwards=false
• 关键词: CRII; CIF; Information; Theoretic; Measures

Despite the growing success of Machine Learning (ML) systems in accomplishing complex tasks, their increasing use in making or aiding consequential decisions that affect people’s lives (e.g., university admission, healthcare, predictive policing) raises concerns about potential discriminatory practices. Unfair outcomes in ML systems result from historical biases in the data used to train them. A learning algorithm designed merely to minimize prediction error may inherit or even exacerbate such biases; particularly when observed attributes of individuals, critical for generating accurate decisions, are biased by their group identities (e.g., race or gender) due to existing social and cultural inequalities. Understanding and measuring these biases-- at the data level-- is a challenging yet crucial problem, leading to constructive insights and methodologies for debiasing the data and adapting the learning system to minimize discrimination, as well as raising the need for policy changes and infrastructural development. This project aims to establish a comprehensive framework for precisely quantifying the marginal impact of individuals’ attributes on accuracy and unfairness of decisions, using tools from information and game theories and causal inference, along with legal and social science definitions of fairness. This multi-disciplinary effort will provide guidelines and design insights for practitioners in the field of fair data-driven automated systems and inform the public debate on social consequences of artificial intelligence.The majority of previous work formulates the algorithmic fairness problem from the viewpoint of the learning algorithm by enforcing a statistical or counterfactual fairness constraint on the learner’s outcome and designing a learner that meets it. As the considered fairness problem originates from biased data, merely adding constraints to the prediction task might not provide a holistic view of its fundamental limitations. This project looks at the fairness problem through different lens, where instead of asking “for a given learner, how can we achieve fairness”?, it asks “for a given dataset, what are the inherent tradeoffs in the data, and based on these, what is the best learner we can design”?. In supervised learning models, the challenge in the proposed problem lies in the complex structures of correlation/causation among individuals’ attributes (covariates), their group identities (protected features), the target variable (label), and the prediction outcome (decision). In analyzing the dataset, the marginal impacts of covariates on the accuracy and discrimination of decisions are quantified from the data, via carefully designed measures accounting for the complex correlation/causation structures among variables and the inherent tension between accuracy and fairness objectives. Subsequently, methods to exploit the quantified impacts in guiding downstream ML systems to improve their achievable accuracy-fairness tradeoff will be investigated. Importantly, the proposed framework provides explainable solutions, where the inclusion of certain attributes in the learning system is explained by their importance for accurate as well as fair decisions.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.

尽管机器学习（ML）系统在完成复杂的任务方面取得了越来越多的成功，但它们在制定或有助于影响人们生活的后果决策中的使用越来越多（例如，大学录取，医疗保健，预测政策）引起了人们对潜在歧视性实践的担忧。 ML系统的不公平结果是由于用于训练它们的数据的历史偏见而产生的。仅仅为了最大程度地减少预测错误而设计的学习算法可能会继承甚至加剧此类偏见。尤其是当观察到的个人属性（对于产生准确的决策至关重要）时，由于现有的社会和文化不平等，其群体身份（例如种族或性别）偏见。在数据级别上理解和衡量这些偏见是一个挑战而又至关重要的问题，导致建设性的见解和方法，用于使数据和调整学习系统以最小化歧视，并提高了对政策变化和基础设施发展的需求。该项目旨在建立一个综合框架，以精确量化个人属性对决策准确性和不公平性的边际影响，并使用信息和游戏理论中的工具以及因果推理，以及法律和社会科学的公平定义。这项多学科的工作将为公平数据驱动的自动化系统领域的从业者提供指南和设计见解，并为人工智能的社会后果提供公开辩论。先前的大多数工作从学习算法的观点中提出算法公平问题，从而实现了统计或对比的公平构成对教学者的构成和构成教学者的构成。由于被认为是公平性问题源于偏见的数据，因此仅在预测任务中添加限制可能无法提供其基本局限性的整体视图。该项目通过不同的镜头来着眼于公平问题，而不是要求“给定的学习者，我们如何实现公平”？它要求“对于给定的数据集，而是数据中的继承权衡是什么，而基于这些数据集，我们可以设计的最好的学习者是什么？在监督的学习模型中，提出的问题的挑战在于个人属性（协变量）之间的相关性/因果关系的复杂结构，其组身份（受保护特征），目标变量（标签）和预测结果（决策）。在分析数据集时，通过仔细设计的措施来考虑变量之间的复杂相关/因果关系结构以及准确性和公平目标之间的继承张力，从数据量化了协变量对决策准确性和歧视的边际影响。随后，将研究指导下游ML系统以提高其成就准确性权衡的方法来利用量化影响的方法。重要的是，拟议的框架提供了可解释的解决方案，其中将某些属性包含在学习系统中是通过其对准确和公正决定的重要性来解释的。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子和更广泛影响的评估来评估的珍贵的支​​持。