FinRegLab Events

Machine Learning Explained: Evidence on the Explainability and Fairness of Machine Learning Credit Models

Virtual Conference April 28, 2022

FinRegLab hosted a virtual conference in April 2022 featuring research being conducted by FinRegLab and Professors Laura Blattner and Jann Spiess of the Stanford Graduate School of Business on the use of machine learning in credit underwriting, with a particular focus on their potential implications for explainability and fairness. 

Machine learning models are drawing increased attention in credit underwriting because of their potential for greater accuracy and, particularly when combined with new data sources, greater financial inclusion. But because the models can be more complicated to analyze and manage, model transparency has become a critical threshold question for both lenders and regulators. The research empirically evaluates the performance and capabilities of currently available tools designed to help lenders develop, monitor, and manage machine learning underwriting models.

Additionally, the conference featured specific panels with subject matter experts and academics on fairness and explainability. The panels explored how tools designed to help lenders explain and manage ML underwriting models can help foster responsible use of complex models to make decisions with high stakes for consumers, firms, and communities.

Welcoming Remarks


Melissa Koide

CEO & Director

Presentation of Research Q&A

The initial panel included a presentation summarizing the research team’s recent white paper, “Machine Learning Explainability & Fairness: Insights from Consumer Lending,”  which evaluates seven proprietary tools as well as several open-source diagnostic methods to understand how their outputs could potentially help lenders in generating individualized consumer disclosures and fair lending compliance. The panel also included discussion by respondents and questions by audience members.


John Morgan

Managing Vice President

Patrick Hall

Principal Scientist

Panel 1 – Explainability

The complexity of models derived by AI/ML algorithms poses fundamental challenges for oversight: How can we gain sufficient insight into how a model makes predictions in order for model users and their regulators to enable oversight and governance? The panel discussed debates over the use of inherently interpretable underwriting models as compared to post hoc diagnostic tools. The panel also considered which explainability challenges and limitations will most likely be the focus for practitioners over the next decade.


Krishnaram Kenthapadi

Chief Scientist

Laura Kornhauser

Co-founder and CEO

Jann L. Spiess

Assistant Professor of Operations, Information & Technology

Scott Zoldi

Chief Analytics Officer

Panel 2 – Fairness

Serious questions exist about the ability of lenders to deploy machine learning underwriting models that meet anti-discrimination and equity expectations. The panel discussed concerns that the use of machine learning underwriting models will increase fair lending risks. Panel members also discussed specific approaches to improving the fairness of underwriting models, such as defining a framework for making fairness-performance tradeoffs and prospects for adversarial debiasing.


Michael Akinwumi

National Fair Housing Alliance (NFHA)

Sri Satish Ambati

CEO and Co-Founder

Related Publications

  • Explainability and Fairness in Machine Learning for Credit Underwriting

    FinRegLab worked with a team of researchers from the Stanford Graduate School of Business to evaluate the explainability and fairness of machine learning for credit underwriting. We focused on measuring the ability of currently available model diagnostic tools to provide information about the performance and capabilities of machine learning underwriting models. This research helps stakeholders… Learn More

About FinregLab

FinRegLab is an independent, nonprofit organization that conducts research and experiments with new technologies and data to drive the financial sector toward a responsible and inclusive marketplace. The organization also facilitates discourse across the financial ecosystem to inform public policy and market practices. To receive periodic updates on the latest research, subscribe to FRL’s newsletter and visit Follow FinRegLab on LinkedIn and Twitter (X). | 1701 K Street Northwest, Suite 1150, Washington, DC 20006