A validation the CFPB never ran: its BISG regression roughly doubled the disparities behind $160M+ in indirect-auto enforcement.

New Reg B drops disparate impact—but its direction-agnostic proxy test may recast debiased 'LDA' credit models as disparate treatment.

The 2025 EO didn't kill disparate impact — SCOTUS settled that in Inclusive Communities. But agency enforcement overreach may have invited the blow.

A recent CFPB publication directs lenders to use race-based debiasing methods for their credit models. Why is this a serious problem?

This article examines the further risks and challenges of using less discriminatory alternative (LDA) credit models amid CFPB scrutiny.

Current credit model disparate impact frameworks dispense justice algorithmically. But are they consistent with applicable fairness laws?

AI delivers rewards, but the C-suite/Board must address the risks of unchecked algorithmic sprawl diluting returns - governance is crucial.

My research suggests that LDA credit models may pose risks to early adopters. What research priorities could put us on a more viable path?

LDA Credit Models, believed to reduce credit barriers, may instead expand credit access inappropriately, thereby creating risks for lenders.

The existence of similarly-predictive models with improved fairness is a foundational pillar of algorithmic debiasing. Is it actually true?