Virtual Conferences: Machine Learning Explained and AI in the Economy

April 27-28, 2022

The U.S. Department of Commerce and National Institute of Standards and Technology, FinRegLab, and the Stanford Institute for Human-Centered Artificial Intelligence (HAI), hosted a virtual conference, “Artificial Intelligence and the Economy: Charting a Path for Responsible and Inclusive AI,” on April 27, 2022.

This symposium brought 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.

The event focused on how these technologies relate to ensuring inclusive economic growth, supporting diversity and financial inclusion, and mitigating risks such as bias and unfairness. It featured presenters and panelists on the cutting edge of researching fairness and explainability in AI, as well as those working to develop policies and frameworks to evaluate and assess the goals of improving the trustworthiness, inclusiveness, and equity of AI deployment.

FinRegLab also hosted a virtual conference on April 28, 2022, titled “Machine Learning Explained: Evidence on the Explainability and Fairness of ML Credit Models.” The event explored research being conducted by FinRegLab and Professors Laura Blattner and Jann Spiess of the Stanford Graduate School of Business on the use of machine learning in credit underwriting, with a particular focus on their potential implications for explainability and fairness.

This research empirically evaluates the performance and capabilities of currently available tools designed to help lenders develop, monitor, and manage machine learning underwriting models. It also provides insight into the ways in which using machine learning can improve the fairness of lending decisions. This includes exploring the ways in which using machine learning techniques can improve options available to lenders in developing underwriting models that deliver greater accuracy and fairness than incumbent models.

Additionally, the conference featured specific panels with subject matter experts and academics on fairness and explainability. The panels explored how tools designed to help lenders explain and manage ML underwriting models can help foster responsible use of complex models to make decisions with high stakes for consumers, firms, and communities.