CAREER: Towards Fairness in the Real World under Generalization, Privacy and Robustness Challenges

人工智能（AI）算法广泛应用于各种现实世界的应用，如社交媒体挖掘和健康信息学。确保人工智能算法的公平性变得越来越重要，以避免放大不平等和加强现有的偏见。尽管近年来公平性算法取得了很大的进展，但在真实的世界中应用时，仍然面临着实用的泛化性、隐私性和鲁棒性等方面的挑战。首先，公平性的性能可以显着下降的分布移位，如域和时间的移位。其次，大多数以前的公平算法需要直接访问确切的人口统计属性，这通常是不可行的，由于人们的意识和法律的规定的隐私。此外，研究表明，解决公平问题可能会增加隐私泄露的风险。第三，恶意行为者可以通过在训练阶段注入中毒样本或在推理阶段操纵数据来放大AI算法的人口统计偏见。该项目的目标是研究上述问题对公平性的影响，并制定有效的解决方案，以确保泛化、隐私和鲁棒性挑战下的公平性。为了实现研究目标，该项目系统地研究了域和时间变化下公平性的关键方向、隐私机制实施和隐私泄露风险下的公平性、偏见放大攻击和防御方法。该项目的成果有助于推进公平人工智能的最新研究，并介绍：（1）从信息理论的角度和元学习框架来确保时间不变公平的领域自适应公平性;（2）在局部差分隐私机制下提高公平性性能的算法，并在最小化隐私泄漏的同时实现公平图学习;（3）在局部差分隐私机制下提高公平性的算法。（3）针对公平性的投毒攻击和规避攻击，以及相应的以模型为中心和以数据为中心的防御方法。更广泛地说，该项目将对改善实践中的公平算法产生直接而强烈的影响，使负责任的数据分析与真实的世界中先进的值得信赖的AI范式。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

Artificial Intelligence (AI) algorithms are widely adopted in various real-world applications such as social media mining and health informatics. It becomes increasingly essential to ensure fairness in AI algorithms to avoid amplifying inequalities and reinforcing existing prejudice. Although fairness algorithms have achieved great progress recently, when deployed in the real world, they still face practical generalization, privacy and robustness challenges. First, the fairness performance can be significantly degraded under distribution shifts such as domain and temporal shifts. Second, most previous fairness algorithms require direct access to the exact demographic attributes, which is usually infeasible due to people's awareness and legal regulations on privacy. Moreover, research indicates that addressing fairness may increase privacy leakage risks. Third, malicious actors can amplify the demographic bias of AI algorithms by injecting poisoning samples in the training stage or manipulating the data in the inference stage. The goal of this project is to investigate the impact of the aforementioned issues on fairness and develop effective solutions to ensure fairness under generalization, privacy and robustness challenges.To achieve the research goal, the project systematically investigates the key directions of fairness under domain and temporal shifts, fairness faced with privacy mechanism enforcement and privacy leakage risks, bias amplification attack and defense methods. The project outcomes help advance state-of-the-art research on fair AI and introduce: (1) fairness in domain adaptation from an information-theoretical perspective and a meta-learning framework to ensure temporal-invariant fairness; (2) algorithms improving fairness performance under local differential privacy mechanism and achieving fair graph learning while minimizing the privacy leakage; and (3) poisoning and evasion attacks on fairness properties, as well as model-centric and data-centric defense methods for such attacks accordingly. More broadly, this project will have an immediate and strong impact on improving fairness algorithms in practices, enabling the responsible data analysis with advanced trustworthy AI paradigms in the real world.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.