Algorithmic models built using machine learning and large volumes of personal data are increasingly used to target consumers with credit offers, assess consumers’ creditworthiness, price credit, provide debt management advice to consumers, resolve credit disputes, and more generally automate the credit lifecycle.
While machine learning and automation offer many benefits for consumers and credit markets, they also carry significant legal risks. A key concern is the risk of unlawful discrimination—disparate treatment and disparate impact—against certain groups of borrowers, particularly women and people of color. In recent years, scholars and policymakers have been paying greater attention to these risks. However, fundamental questions about the scope and meaning of discrimination in automated, algorithmic consumer credit markets, and “fair lending” more broadly, remain undertheorized and unresolved.
To learn more and register for the Symposium (in person or on-line) please go to: https://www.eventbrite.com/e/automating-bias-cardozo-law-review-2023-symposium-tickets-453239460427
