An Overview of Trustworthy AI: Advances in IP Protection, Privacy-Preserving Federated Learning, Security Verification, and GAI Safety Alignment

Abstract

AI has undergone a remarkable evolution journey marked by groundbreaking milestones. Like any powerful tool, it can be turned into a weapon for devastation in the wrong hands. Understanding that no model is perfect, trustworthy AI is initiated with an intuitive aim to mitigate the harm it can inflict on people and society by prioritizing socially responsible AI ideation, design, development, and deployment towards effecting positive changes. The scope of trustworthy AI is encompassing, covering qualities such as safety, security, privacy, transparency, explainability, fairness, impartiality, robustness, reliability, and accountability. This overview paper anchors on recent advances in four research hotspots of trustworthy AI with compelling and challenging security, privacy, and safety issues. The topics discussed include the intellectual property protection of deep learning and generative models, the trustworthiness of federated learning, verification and testing tools of AI systems, and the safety alignment of generative AI systems. Through this comprehensive review, we aim to provide readers with an overview of the most up-to-date research problems and solutions. By presenting the rapidly evolving factors and constraints that motivate the emerging attack and defense strategies throughout the AI life-cycle, we hope to inspire more research effort into guiding AI technologies towards beneficial purposes with greater robustness against malicious use intent.