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)系统在完成复杂任务方面越来越成功，但它们在做出或帮助做出或帮助做出影响人们生活的结果决策(例如，大学入学、医疗保健、预测性警务)方面的使用越来越多，这引发了人们对潜在歧视性做法的担忧。ML系统的不公平结果是由用于训练它们的数据的历史偏见造成的。仅为最大限度地减少预测误差而设计的学习算法可能会继承甚至加剧这种偏见；特别是当观察到的对做出准确决策至关重要的个人属性由于现有的社会和文化不平等而因其群体身份(例如种族或性别)而产生偏见时。在数据层面上理解和衡量这些偏见是一个具有挑战性但又至关重要的问题，这将导致提出建设性的见解和方法，以消除数据的偏见，调整学习系统以尽量减少歧视，并提高政策变化和基础设施发展的必要性。该项目旨在建立一个全面的框架，利用信息和博弈论、因果推理以及法律和社会科学对公平的定义，精确量化个人属性对决策准确性和不公平的边际影响。这一多学科的努力将为公平数据驱动的自动化系统领域的实践者提供指导和设计见解，并为公众关于人工智能的社会后果的辩论提供信息。以前的大多数工作从学习算法的角度提出算法公平问题，方法是对学习者的结果实施统计或反事实公平约束，并设计满足该约束的学习者。由于所考虑的公平性问题源于有偏见的数据，仅仅在预测任务中添加约束可能不能提供对其基本限制的整体看法。这个项目通过不同的视角来看待公平问题，它不是问“对于给定的学习者，我们如何实现公平？”，它问的是“对于给定的数据集，数据中的内在权衡是什么，基于这些，我们可以设计的最佳学习者是什么？”在有监督学习模型中，所提出问题的挑战在于个体属性(协变量)、其群体身份(受保护特征)、目标变量(标签)和预测结果(决策)之间的关联/因果关系的复杂结构。在分析数据集时，通过精心设计的措施，从数据中量化协变量对决策准确性和区分性的边际影响，其中考虑到变量之间复杂的相关性/因果关系结构以及准确性和公平目标之间的内在张力。随后，将研究如何利用量化影响来引导下行最大似然系统提高其可实现的精度-公平性折衷。重要的是，建议的框架提供了可解释的解决方案，其中学习系统中包含的某些属性是通过它们对准确和公平决策的重要性来解释的。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。