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?

How Does the Updated 2020 Census Data Impact the Accuracy of Race/Ethnicity Proxies and Fair Lending Disparity Estimates?

The Order creates potentially serious conflicts on data and information sharing and fair lending compliance risk management responsibilities

On the 12th anniversary of US regulatory guidance on model risk management, I discuss 10 attributes of highly-effective model risk managers.

In this blog post, I review the promises and potential pitfalls of Meta's Variance Reduction System - a new tool to address algorithmic bias

Can we really achieve increased credit model fairness with minimal trade-offs?

How are macroeconomic conditions impacting AI credit model decisions and what does this imply for popular algorithmic de-biasing practices?

CFPB Circular 2022-03 raises questions about the use of Explainable AI to produce Adverse Action reasons. Here, I explore the key risks.

Where are federal regulators and enforcement agencies heading with their clear concerns over algorithmic bias in financial services?

I examine the shifting winds in the CFPB's sentiment towards AI-based lending and describe two potential dangers brewing on the horizon.

In this post, I highlight 6 critical open questions related to AI bias for which consumer lenders need regulatory guidance.

What do we know about its properties, and are there important risks or limitations we should be evaluating now in anticipation of its use?

In this post, I do a deeper dive into the important interdependency of credit model explainability and fairness.

I lay out a few potential "under-the-radar" risks of AI credit models' underlying technologies and use of alternative data.

What's in store for lenders in 2022 as a new class of financial regulatory leaders ramp up their supervision and enforcement activities?