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AI in Credit Decision-Making Is Promising, but Beware of Hidden Biases, Fed Warns | Lowenstein Sandler LLP

To embed, copy and paste the code into your website or blog: As financial services firms increasingly turn to artificial intelligence (AI), banking regulators warn that despite their astonishing capabilities, these tools must be relied upon with caution. Last week, the Board of Governors of the Federal Reserve (the Fed) held a virtual AI Academic Symposium to explore the application of AI in the financial services industry. Governor Lael Brainard explained that “particularly as financial services become more digitized and shift to web-based platforms,” a steadily growing number of financial institutions have relied on machine learning to detect fraud, evaluate credit, and aid in operational risk management, among many other functions.[i]

Supporting Responsible Use Of AI And Equitable Outcomes In Financial Services – Federal Reserve Governor Lael Brainard At The AI Academic Symposium Hosted By The Board Of Governors Of The Federal Reserve System, Washington, D C (Virtual Event)

Supporting Responsible Use Of AI And Equitable Outcomes In Financial Services – Federal Reserve Governor Lael Brainard At The AI Academic Symposium Hosted By The Board Of Governors Of The Federal Reserve System, Washington, D.C. (Virtual Event) Date 12/01/2021 Today s symposium on the use of artificial intelligence (AI) in financial services is part of the Federal Reserve s broader effort to understand AI s application to financial services, assess methods for managing risks arising from this technology, and determine where banking regulators can support responsible use of AI and equitable outcomes by improving supervisory clarity.1 The potential scope of AI applications is wide ranging. For instance, researchers are turning to AI to help analyze climate change, one of the central challenges of our time. With nonlinearities and tipping points, climate change is highly complex, and quantification for risk assessments requires the analysis of vast amounts of data, a task

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