Data Disquiet: Concerns about the Governance of Data for Generative AI

March 20, 2024

Artist's Illustration of AI

In a new paper for the Centre for International Governance Innovation, Research Professor Susan Aaronson examines generative AI and governance. The growing popularity of large language models (LLMs) has raised concerns about their accuracy. These chatbots can be used to provide information, but it may be tainted by errors or made-up or false information (hallucinations) caused by problematic data sets or incorrect assumptions made by the model. The questionable results produced by chatbots has led to growing disquiet among users, developers and policy makers. Aaronson argues that policy makers need to develop a systemic approach to address these concerns. The current piecemeal approach does not reflect the complexity of LLMs or the magnitude of the data upon which they are based, therefore, Aaronson recommends incentivizing greater transparency and accountability around data-set development.

Interested in learning more? The complete paper is available online for free at the Centre for International Governance Innovation.