The CFPB recently published a blog post titled, “Innovation spotlight: Providing adverse action notices when using AI/ML models.”
The blog post primarily recycles information from the Bureau’s annual fair lending report issued in May 2020. The Bureau indicates that artificial intelligence (AI) and a subset of AI, machine learning (ML), is an area of innovation that it is monitoring. It notes that “industry uncertainty about how AI fits into the existing regulatory framework may be slowing its adoption, especially for credit underwriting.” The Bureau observes that “one important issue is how complex AI models address the adverse action notice requirements in the [ECOA] and the [FCRA]” and that “there may be questions about how institutions can comply with these requirements if the reasons driving an AI decision are based on complex interrelationships.”
As it did in the fair lending report, the Bureau comments that “the existing regulatory framework has built-in flexibility that can be compatible with AI algorithms” and repeats the two examples of such flexibility given in the report: (1) the absence of a requirement for a creditor, when giving specific reasons for adverse action, to describe how or why a disclosed factor adversely affected an application, or, for credit scoring systems, how the factor relates to creditworthiness, and (2) the absence of a requirement for a creditor to use any particular list of reasons.
The Bureau again encourages entities to consider using its new innovation policies (e.g. No-Action Letter and Trial Disclosure Policies) to address potential compliance issues. It also states that it intends “to leverage experiences gained through the innovation policies to inform policy,” and indicates that such experiences “may ultimately be used to help support an amendment to a regulation or its Official Interpretation.” FULL ARTICLE