This presentation argues that a reflexive legal approach to understanding trade secrets law and artificial intelligence law is a pragmatic response to the challenges posed to public health in the context of emergence of AI.

As AI continues to transform sectors including healthcare, its applications—ranging from diagnosis and clinical care to drug development, disease surveillance, outbreak response, and health systems management—are becoming increasingly central to public health strategies.

Transparency is critical to ensuring accountability and trust in public health, and it is also a widely endorsed ethical principle in AI governance. Achieving transparency in AI systems involves enhancing explainability and interpretability—such as through the disclosure of source code, data, model development processes, decision-making pathways, and thorough documentation prior to deployment.

This demand for transparency may conflict with trade secrets law, which protects confidential business information that holds economic value and is subject to reasonable measures to maintain its secrecy. In the AI domain, proprietary algorithms, training data, and development methods may all qualify as trade secrets. This tension between the public’s need for transparency and the private sector’s interest in maintaining secrecy underscores the complexity of governing AI in health-related applications.

The intersection of artificial intelligence (AI), trade secrets, and public health presents complex challenges that underscores the understanding of trade secrets protection and public health concern in the context of AI’s emergence. To navigate this intersection, the presentation draws on the concept of reflexive law, which emphasises self-regulation, stakeholder participation, flexibility, and the creation of legal frameworks that enable industries to develop and enforce their own standards. Reflexive law avoids rigid mandates, instead encouraging adaptive, context-sensitive norms that evolve alongside technological and social developments. By applying this framework, the presentation explores how the legal system can support both trade secret protection and the public health imperative for transparency in AI, ultimately proposing a balanced, participatory, and adaptable regulatory approach.