The Intersection of AI Safety, Privacy, and Trust: Technical Foundations for Responsible AI Systems

Abstract

The article on AI safety, privacy, and trustworthiness has explored the most critical issues that confront the advanced machine learning systems as they continue to be more embedded in the societal infrastructure. This technical article is a synthesis of the research in adversarial robustness, out-of-distribution detection, and uncertainty estimation as baseline safety controls. The article examines the privacy-saving strategies such as differential privacy, secure multiparty computation, homomorphic encryption, and federated learning, and discusses their feasibility in real life versus their theoretical promises. Regulatory efforts, including the EU AI Act, NIST AI Risk Management Framework, are evaluated in addition to industry-driven standardization efforts. The article, through case studies in autonomous vehicles, healthcare diagnostics, and large language models, throws light on domain-specific expressions of safety and privacy issues. The article recommends the lifecycle consideration of protection controls, starting with dataset curation to the post-deployment control, and that AI protection should be governed by layers of defenses that integrate complementary strategies. The results highlight the need to be interdisciplinary in the cooperation of the technical experts with the specialists in the domain to keep the AI systems on the right track and achieve their intended benefits.