WASHINGTON—While the use of artificial intelligence and machine learning technologies by the financial services sector can transform systems and processes, it also increases bias, inclusion and risk management concerns, according to witnesses appearing before a hearing by the House Committee on Financial Services’ Artificial Intelligence Task Force.
“When done right, artificial intelligence can mean innovative underwriting models that allow millions more people access to credit and financial services,” said Rep. Bill Foster (D-Ill), chair of the task force on artificial intelligence, in opening remarks at the hearing.
For example, artificial intelligence can be used to better detect fraud and money-laundering, and regulators use it to improve market surveillance and police bad actors, he said.
Other Questions Raised
However, Foster said artificial intelligence also raises key questions, such as, “How can we be sure that artificial intelligence credit underwriting models are not biased? Who is accountable if artificial intelligence algorithms are just a black box that nobody can explain when it makes a decision?” according to a report from Business Insurance.
Artificial intelligence also runs on enormous amounts of data raising concerns on where that data comes from and how it is protected, he added.
“Machine learning algorithms have become more sophisticated and pervasive tools for automated decision making,” said Dr. Nicol Turner-Lee, fellow, governance studies, Center for Technology Innovation, Brookings Institution in Washington, D.C., during testimony, Business Insurance noted. “These models make inferences from data about people including their identity, their demographic attributes and likely future preferences.”
Despite the models’ greater facilitation of efficiency and cognition, “the online economy has not resolved the issue of racial bias,” she said.
‘Troubling and Dangerous’
These issues are “troubling and dangerous,” Turner-Lee testified, in particular for African Americans and Latinos who have been “ill-served within the financial services market.”
“Artificial intelligence offers the possibility of greater financial inclusion, but its rapid growth in an already complex financial system presents major challenges regarding regulation and policymaking, risk management, as well as ethical, economic and social hurdles,” said Dr. Bonnie Buchanan, head of school of finance and accounting and professor of finance, Surrey Business School at the University of Surrey in the U.K., in testimony.
Machine learning algorithms can also potentially introduce bias and discrimination, she said.
“Deep learning provides predictions, but it does lack insight as to how the variables are being used to reach these predictions,” Buchanan said.
Hiring and credit scoring algorithms can exacerbate inequities due to biased data, she said.
Despite its benefits, machine learning raises “serious risks” for institutions and consumers, said Dr. Douglas Merrill, founder and CEO, ZestFinance.
“Machine learning models are opaque and inherently biased. Lenders put themselves, consumers and the safety and soundness of our entire financial system at risk if they do not appropriately validate and monitor machine learning models,” said Merrill.
“Getting this mix right, enjoying machine learning’s benefits while employing responsible safeguards is difficult,” he said.