Standardizing Intelligence: Aligning Generative AI for Regulatory and Operational Compliance
It addresses the problem of ensuring generative AI compliance with standards for industries like engineering, legal, healthcare, and education, but is incremental as it focuses on assessment and recommendations rather than new solutions.
The paper assesses the criticality of standards across domains and grades the compliance capabilities of state-of-the-art generative AI models, arguing that aligning GenAI with standards can strengthen regulatory and operational compliance.
Technical standards, or simply standards, are established documented guidelines and rules that facilitate the interoperability, quality, and accuracy of systems and processes. In recent years, we have witnessed an emerging paradigm shift where the adoption of generative AI (GenAI) models has increased tremendously, spreading implementation interests across standard-driven industries, including engineering, legal, healthcare, and education. In this paper, we assess the criticality levels of different standards across domains and sectors and complement them by grading the current compliance capabilities of state-of-the-art GenAI models. To support the discussion, we outline possible challenges and opportunities with integrating GenAI for standard compliance tasks while also providing actionable recommendations for entities involved with developing and using standards. Overall, we argue that aligning GenAI with standards through computational methods can help strengthen regulatory and operational compliance. We anticipate this area of research will play a central role in the management, oversight, and trustworthiness of larger, more powerful GenAI-based systems in the near future.