The Federal Reserve Bank of Cleveland and the Consumer Finance Institute of the Federal Reserve Bank of Philadelphia hosted a virtual conference on the application of artificial intelligence in consumer finance. 

Rigorous and robust conversation held over the course of two days helped define what are fair outcomes, both in the context of recent advances in computer science and in practical application under the law. Specifically, we explored how algorithms can be evaluated and assessed as fair in the pricing of and access to consumer lending and payments. Download the conference program and agenda.

Day 1: Defining and Verifying Algorithmic Fairness

Algorithmic Fairness, Causality, and Interpretability


Moderator: Minchul Shin, Federal Reserve Bank of Philadelphia

Practical Challenges to Recent Advances in Machine Learning


Moderator: Jeanne Rentezelas, Federal Reserve Bank of Philadelphia

Day 2: The Past as Prologue

Possible Discrimination and Its Uncertain Mechanisms


  • Alexander D'Amour, Google Research
  • Jann Speiss, Stanford University (presentation)
  • Matt Kusner, University College London (presentation)

Moderator: David Lynch, Board of Governors

Keynote: Lessons From the Past

Presenter: Robert Avery, Federal Housing Finance Agency (presentation)

Statistical Lessons in Identifying Fairness


Moderator: Dionissi Aliprantis, Federal Reserve Bank of Cleveland

The views expressed in the presentations are solely those of the authors and do not necessarily reflect the views of the Federal Reserve Bank of Philadelphia or the Federal Reserve System.