FAI: Towards a Computational Foundation for Fair Network Learning

网络学习和挖掘在计算机科学、物理学、社会科学、管理学、神经科学、土木工程和电子商务等许多学科中发挥着关键作用。在这个领域几十年的研究已经提供了丰富的理论、算法和开源系统来回答谁/什么类型的问题。例如，谁在社交网络中最有影响力？在电子商务平台上，我们应该向给定的用户推荐什么商品？什么样的Twitter海报可能会走红？谁可以被分组到同一个在线社区？用户之间的哪些金融交易看起来可疑？回答这些问题的最先进的技术已经广泛应用于各种现实世界的应用中，通常具有很强的经验表现和坚实的理论基础。尽管网络学习取得了显著的进步，但一个基本问题在很大程度上仍处于萌芽状态：我们如何使网络学习的结果和过程可解释、透明和公平？这个问题的答案在可解释性、透明度和公平性方面有利于各种高影响力的基于网络学习的应用，包括社会网络分析、神经科学、团队科学和管理、智能交通系统、关键基础设施和区块链网络。这个项目为网络学习带来了一个转变，从回答谁和什么到回答如何和为什么。它发展了网络学习背景下的计算理论、算法和原型系统，形成了公平网络学习的三个关键支柱。第一个支柱（解释）侧重于向最终用户解释网络学习的结果和过程，而最终用户通常不是机器学习专家。特别是，该项目开发了理论和指标来量化网络学习解释的质量。在此基础上，通过仔细平衡模型保真度和模型可解释性，为网络学习算法带来可解释性。第二个支柱（审计）使网络学习过程对最终用户透明，重点是演示给定网络学习算法的学习结果如何与底层网络结构相关。特别是，它开发了一种新的公平性措施，以适应网络学习的非独立和同分布性质。基于这种新的公平性度量，开发了一个算法框架来审计各种网络学习算法。第三个支柱（去偏见）探讨如何减轻潜在的偏见，以确保公平的网络学习。支撑这些支柱的是一个人在循环中的可视化分析框架，以支持用户识别和减轻网络学习中的偏见。通过将研究成果融入到研究团队开发的课程和暑期项目中，该项目培养学生重视公平精神。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Network learning and mining plays a pivotal role across a number of disciplines, such as computer science, physics, social science, management, neural science, civil engineering, and e-commerce. Decades of research in this area has provided a wealth of theories, algorithms and open-source systems to answer who/what types of questions. For example, who is the most influential in a social network? What items shall we recommend to a given user on an e-commerce platform? What Twitter poster is likely to go viral? Who can be grouped into the same online community? What financial transactions between users look suspicious? The state-of-the-art techniques on answering these questions have been widely adopted in various real-world applications, often with a strong empirical performance as well as a solid theoretic foundation. Despite the remarkable progress in network learning, a fundamental question largely remains nascent: how can we make network learning results and process explainable, transparent, and fair? The answer to this question benefits a variety of high-impact network learning based applications in terms of their interpretability, transparency and fairness, including social network analysis, neural science, team science and management, intelligent transportation systems, critical infrastructures, and blockchain networks.This project takes a shift for network learning, from answering who and what to answering how and why. It develops computational theories, algorithms and prototype systems in the context of network learning, forming three key pillars of fair network learning. The first pillar (interpretation) focuses on explaining the network learning results and process to end users, who are often not machine learning experts. In particular, this project develops theory and metrics to quantify the quality of explanations for network learning. Based on that, it brings explainability to network learning algorithms by carefully balancing the model fidelity and model interpretability. The second pillar (auditing) makes the network learning process transparent to end-users, focusing on demonstrating how the learning results of a given network learning algorithm relate to the underlying network structure. In particular, it develops a new fairness measure to accommodate the non-independent-and-identically-distributed nature of network learning. Based on this new fairness measure, it develops an algorithmic framework to audit a variety of network learning algorithms. The third pillar (de-biasing) explores how to mitigate potential biases to ensure fair network learning. Underpinning these pillars is a human-in-the-loop visual analytics framework to support users in identifying and mitigating bias in network learning. By assimilating the research outcome into the courses and summer programs that the research team has developed, this project trains students to value the spirit of fairness.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.