Rapidly developing AI-powered technology is making it easier to appropriate the public sector’s financial information for proprietary uses. Businesses that slice and dice this data should be renters, not owners.
Earlier this year, I explained how a new federal law, the Financial Data Transparency Act (FDTA), will require states and localities to prepare financial information in machine-readable forms. Since then, there has been a lot of back-and-forth between the FDTA bill’s sponsors and some of the professional associations about implementation, the role of the federal agencies assigned to receive this information, the implementation timetable, and the scope of what’s to be covered. Critics call it a Procrustean solution in search of a problem.
While all that’s been going on, however, a tectonic shift in information technology has taken place featuring generative artificial intelligence systems, machine learning and rapidly evolving large language models that surpass the buzzy ChatGPT facility that is now so familiar to many. It’s now a sprint for these AI systems to develop superior capabilities to ingest information of all kinds, including images, and to create and manipulate databases, compile information in user-friendly formats for analysts and decision-makers, and deliver actionable analytics that are increasingly faster, cheaper and more insightful. Literally billions of dollars will be invested in this new AI technology in coming years.
The ownership of databases, analyses and related intellectual property scarfed up and refabricated by these systems is a burning issue that will spill into the governmental finance arena in short time. There is a non-trivial risk of concentrated monopoly or oligopoly control over powerfully AI-curated versions of what starts out as public information but quickly becomes private intellectual property when compiled, dissected, analyzed and commercialized by a proprietary machine learning system.
governing.com
by Girard Miller
Nov. 14, 2023