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

职业：在泛化、隐私和稳健性挑战下实现现实世界的公平

• 批准号: 2339198
• 负责人: Kai Shu
• 金额: $ 49.98万
• 依托单位: Illinois Institute of Technology
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-04-15 至 2029-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2339198&HistoricalAwards=false
• 关键词: CAREER; Towards; Fairness; Real; World

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.

人工智能（AI）算法在社交媒体采矿和健康信息学等各种现实应用中广泛采用。确保AI算法中的公平性避免放大不平等并加剧现有偏见的公平性变得越来越重要。尽管公平算法最近取得了巨大的进步，但是当部署在现实世界中时，它们仍然面临实际的概括，隐私和鲁棒性挑战。首先，在分配变化（例如域和时间变化）下，公平性能可以显着降低。其次，大多数以前的公平算法都需要直接访问确切的人口统计属性，由于人们对隐私的意识和法律法规，通常是不可行的。此外，研究表明，解决公平可能会增加隐私泄漏风险。第三，恶意参与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扩大AI算法的人口偏见。该项目的目的是调查上述问题对公平性的影响，并开发有效的解决方案，以确保在概括，隐私和鲁棒性挑战下公平性。为了实现研究目标，该项目系统地研究了领域和时间上的公平和时间上的公平方向，具有隐私机制的操作机制和隐私机制的泄漏风险和危险的辩护和辩护方法。该项目结果有助于推进有关公平AI的最新研究，并介绍：（1）从信息理论的角度和元学习框架中域中的公平性适应性，以确保时间不变的公平性； （2）算法在当地的差异隐私机制下改善公平性能并实现公平的图形学习，同时最大程度地减少隐私泄漏； （3）对公平性质的中毒和逃避攻击，以及以模型为中心和以数据为中心的防御方法。从更广泛的角度来看，该项目将对改善实践中的公平算法产生直接而强大的影响，从而在现实世界中使用高级可信赖的AI范式实现负责任的数据分析。该奖项反映了NSF的法定任务，并认为通过基金会的知识分子和更广泛的影响来评估Criteria CRITERIA CRITERIA。