In December 2020, Upstart Network (“Upstart”), the NAACP Legal Defense Fund (“LDF”), and the Student Borrower Protection Center (“SBPC”) entered into an agreement to appoint Relman Colfax to serve as an independent fair lending Monitor to evaluate and make recommendations regarding the fair lending implications of Upstart’s lending platform, and to issue a series of periodic reports on its findings and recommendations. Those reports are published below, upon release.
Upstart’s lending platform relies on Machine Learning-based Artificial Intelligence (“ML” and “AI”) models and non-traditional applicant data—including data related to borrowers’ education—to underwrite and price consumer loans. LDF and the SBPC raised concerns with Upstart that the use of educational criteria can lead to discriminatory lending outcomes, particularly for communities of color, leading to the appointment of an independent fair lending Monitor.
In April 2021, we issued an Initial Report, which provides a summary of legal principles and fair lending testing, and a descriptive history of the events leading up to the Monitorship.
On November 10, 2021, we issued a public Second Report, providing further detail regarding the methodology and fair lending tests conducted at that time.
The Third Report, issued in September 2022, explains application of those tests to a recent version of Upstart’s Model, including identifying what would likely have been a viable less discriminatory alternative model. Before the analyses were completed, Upstart updated its Model. Accordingly, instead of recommending adoption of that specific less discriminatory alternative model, the Report recommends that Upstart apply the methodologies to its existing algorithms and resulting Model or to any imminent upcoming model updates, and to future model updates.
On March 27, 2024, Relman Colfax published the Fourth and final public Report.
The Final Report describes our findings throughout the monitorship, our related recommendations, and responsive enhancements Upstart made to its fair lending testing protocols. Despite extended discussions and analyses, the Parties remain at an impasse over the appropriate and legally required methodology for assessing whether the performance of a potential less discriminatory alternative model would be comparable to the performance of an existing model. The final Report explains that impasse and the importance that resolving it has for ensuring fair lending testing of models is a meaningful exercise. The Report also notes agreement by the Parties on the contributions of the monitorship to public dialogue about algorithmic discrimination, and it concludes by providing recommendations from the Parties and the Monitor to lenders, regulators, and policymakers.
In our capacity as Monitor we engaged Sentrana to serve as a consultant. Sentrana is a leading firm in the field of machine learning and artificial intelligence. We have also engaged Dr. Bernard Siskin of BLDS. Dr. Siskin is an expert on the use of statistical analyses to measure discrimination in the financial services industry.