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

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.