The authors explore the implications of model multiplicity – the phenomenon in the development of machine learning models that produces several model specifications for a given task that differ in various ways but deliver equal accuracy.

FinRegLab, in partnership with the U.S. Department of Commerce, the National Institute of Standards and Technology (NIST), and the Stanford Institute for Human-Centered Artificial Intelligence (HAI), are hosting a symposium, bringing together leaders from government, industry, civil society, and academia to explore potential opportunities and challenges posed by artificial intelligence and machine learning deployment across different economic sectors, with a particular focus on financial services and healthcare.