Existing governance structures for data and information in Canada-based research institutions are varied and often overlapping. The gaps in governance that result from this varied system leave vulnerabilities that could be exploited through the implementation of machine learning/artificial intelligence (ML/AI) tools or could leave ambiguities about the right to use certain data or metadata in developing or refining ML/AI models. Valuable but underutilized data within research institutions is particularly at risk of being accessed and used by third-party ML/AI tool developers without institutions being properly compensated. The sector requires binding standards for ML/AI deployment, alongside broad strategic planning, the promotion of safe experimentation with ML/AI tools and the development of frameworks for institutions to mobilize and exchange their data.
