Why Ontario Digital Service couldn't procure '98% safe' LLMs (15M Canadians) (rosetta-labs-erb.github.io)

The Ontario Digital Service has revealed critical insights into why high-stakes institutions struggle to adopt probabilistic AI tools, particularly in regulated sectors like healthcare and finance. The challenge lies not in the sophistication of models but in a lack of governing frameworks that enable decision-makers to defend their choices in the event of failures. The article's author emphasized the need for architectural governance primitives, introducing the concept of an Authority Boundary Ledger, which mechanically filters the capabilities an AI model can access based on institutional constraints. This approach not only safeguards compliance but also increases institutional trust in deploying AI solutions. This innovation is significant for the AI/ML community because it directly addresses the barriers preventing widespread adoption of advanced AI technologies in environments where failure could lead to severe consequences, such as public scandals or legal repercussions. The proposed system operates without changing the underlying AI model but rather improves governance by ensuring the model cannot even conceive of certain actions, such as accessing sensitive data. This reference architecture could potentially streamline AI procurement processes and enable institutions to leverage cutting-edge AI while maintaining strict compliance standards, ultimately paving the way for innovative applications in high-stakes domains.