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FRB Governor Promotes Financial Inclusion through AI's picture
Commentary by Steven Lofchie

At an AI Academic Symposium, Federal Reserve Board Governor Lael Brainard compared the potential benefits and the regulatory risk of artificial intelligence ("AI") in financial services.

On the benefits of AI, Ms. Brainard focused on those for "credit invisible" individuals who may not have produced data that is usable by existing credit scoring methodologies. These individuals could be materially benefited by new methodologies that take account of a wider range of data.

Among the regulatory risks, Ms. Brainard warned of the "black box problem" of machine learning. She cautioned that human beings can input data into a model and can examine the outputs, but the process between the start and finish is opaque. Ms. Brainard explained that the lack of model transparency, combined with an algorithm's levels of "explainability," will affect banks in numerous ways. Ms. Brainard gave the following examples:

  • a compliance officer's ability to understand how a model affects operational risk management; and

  • a lender's ability to provide the required explanation to a consumer following a decision to decline consumer credit to them based on a machine learning model.

Ms. Brainard urged regulators to "provide appropriate expectations and adjust those expectations as the use of AI in financial services . . . evolve[s]." Ms. Brainard stated that the FRB is exploring additional supervisory clarity to facilitate the "responsible adoption" of AI, and encouraged stakeholders to provide additional feedback.


Ms. Brainard offers an example of how credit rejections may be explained, provided that the regulators are willing to accept the explanation. A bank may input data that is similar to, but not identical to, the data of a customer whose application has been rejected, and see if a change in the data results in a change in the credit determination. For that to be an acceptable means for a bank to explain a credit rejection, the regulators must be willing to accept the possibility that it is not always obvious why a black box may view a particular bit of data as being negative or positive, or why that data may be viewed as positive or negative in combination with other data specific to that customer.

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