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Still, the majority concluded it would be most appropriate to loosen policy at the upcoming Fed meeting in September, as long as economic data continued to come in as expected. The central bank lowers rates to ensure it doesn’t unnecessarily restrict growth.

Many participants commented that monetary policy continued to be restrictive, although they expressed a range of views about the degree of restrictiveness, and a few participants noted that ongoing disinflation, with no change in the nominal target range for the policy rate, by itself results in a tightening in monetary policy, the minutes said.

The Fed’s decision that month to keep interest rates steady proved itself consequential, as it was soon followed by a weaker-than-expected jobs report in early August.

The data triggered fears that interest rates were, in fact, too high, pulling the economy into recessionary territory. According to July’s minutes, officials were concerned about easing labor market conditions prior to the report, with some noting slipping data prints could transition to a more serious deterioration.

The report briefly sparked expectations for an emergency rate cut, or at least for a deeper interest rate reduction in September. However, concern eased through the rest of this month, as jobless claims fell back to a five-week low.

With the Fed’s next meeting scheduled to start September 17, virtually no one expects interest rates to stay unchanged. Instead, futures markets indicate a 61.5% chance of a 25-basis point cut that month, before policy is further reduced later in the year.

Putting AI to use in mortgage lending decisions could lead to discrimination against Black applicants, according to new research. But researchers say there may be a surprisingly simple solution to mitigate this potential bias.

In an experiment using leading commercial large language models (LLMs) to evaluate loan application data, Lehigh researchers found that LLMs consistently recommended denying more loans and charging higher interest rates to Black applicants compared to otherwise identical white applicants.

This finding suggests that LLMs are learning from the data they are trained on, which includes a history of racial disparities in mortgage lending, and potentially incorporating triggers for racial bias from other contexts, said Donald Bowen III, assistant professor of finance in the College of Business and one of the authors of the study, available as a working paper on SSRN.

The study used real mortgage application data, drawn from a sample of 1,000 loan applications included in the 2022 Home Mortgage Disclosure Act (HMDA) dataset, to create 6,000 experimental loan applications. In the experiment, researchers manipulated race and credit score variables to determine their effects.

The results were stark: Black applicants consistently faced higher barriers to homeownership, even when their financial profiles were identical to white applicants.

Based on the experimental results using OpenAI’s GPT-4 Turbo LLM, Black applicants would, on average, need credit scores approximately 120 points higher than white applicants to receive the same approval rate, and about 30 points higher to receive the same interest rate.

Models also exhibited bias against Hispanic applicants, generally to a lesser extent than against Black applicants.

The bias against minority applicants was highest for riskier applications that had a low credit score, high debt-to-income ratio, or high loan-to-value ratio.

Researchers also tested other LLMs, including OpenAI’s GPT 3.5 Turbo (2023 and 2024) and GPT 4, as well as Anthropic’s Claude 3 Sonnet and Opus, and Meta’s Llama 3-8B and 3-70B.

Bias was generally consistent across the spectrum of LLMs in regard to interest rate recommendations. However, researchers found high variation in approval rates produced by different models.

ChatGPT 3.5 Turbo was found to show the highest discrimination, while ChatGPT 4 (2023) exhibited virtually none.

It’s somewhat surprising to see racial bias, given the efforts LLM creators take to reduce bias overall combined with the large amount of regulations relating to fair lending, Bowen said, noting that the training data of these models almost certainly includes federal regulations prohibiting the use of race as a factor in making lending decisions.

But even more surprising was the ability to remove persistent bias in results with a simple solution — instructing the LLM to use no bias in making decisions.

When the LLMs were instructed to ignore race in their decision-making, the racial bias virtually disappeared.

It didn’t partly reduce the bias, or overcorrect. It almost exactly undid it, Bowen said.