on source data with limited history, even if in the age of big data, these data sets are broad in scope. expectations for banks recognizing that ai presents promise and pitfalls, as a banking regulator, the federal reserve is committed to supporting banks ’ efforts to develop and use ai responsibly to promote a safe, fair, and transparent financial services marketplace. as regulators, we are also exploring and understanding the use of ai and machine learning for supervisory purposes, and therefore, we too need to understand the different forms of explainability tools that are available and their implications. to ensure that society benefits from the application of ai to financial services, we must understand the potential benefits and risks, and make clear our expectations for how the risks can be managed effectively by banks. regulators must provide appropriate expectations and adjust those expectations as the use of ai in financial services and our understanding of its potential and risks evolve. 20 the federal reserve ’ s model risk management guidance ( sr 11 - 7 ) establishes an expectation that models used in banking are conceptually sound or “ fit for purpose. ” sr 11 - 7 instructs that when evaluating a model, supervised institutions should consider the “ [ t ] he design, theory, and logic underlying the model. ” the model risk management guidance discusses in detail the tools banks rely on to help establish the soundness of their models, such as back - testing and benchmarking and other outcomes - based tests. to that end, we are exploring whether additional supervisory clarity is needed to facilitate responsible adoption of ai. it is important that we hear from a wide range of stakeholders — including financial services firms, technology companies, consumer advocates, civil rights groups, merchants and other businesses, and the public. the federal reserve has been working with the other banking agencies on a possible interagency request for information on the risk management of ai applications in financial services. today ’ s symposium serves to introduce a period of seeking input and hearing feedback from a range of external stakeholders on this topic. it is appropriate to be starting with the academic community that has played a central role in developing and scrutinizing ai technologies. i look forward to hearing our distinguished speakers ’ insights on how banks and regulators should think about the opportunities and challenges posed by ai.

, “ interagency statement on the use of alternative data in credit underwriting, ” https : / / www. federalreserve. gov / supervisionreg / caletters / ca % 201911 % 20letter % 20attachement % 20interagency % 20statement % 20on % 20the % 20use % 20of % 20alternative % 20data % 20in % 20credit % 20underwriting. pdf. kenneth p. brevoort, philipp grimm, and michelle kambara, data point : credit invisibles ( washington : consumer financial protection bureau, may 2015 ), https : / / files. consumerfinance. gov / f / 201505 _ cfpb _ data - pointcredit - invisibles. pdf. federal advisory council, which includes a range of banking institutions from across the country, recently noted that nontraditional data and the application of ai have the potential “ to improve the accuracy and fairness of credit decisions while also improving overall credit availability. ” 8 to harness the promise of machine learning to expand access to credit, especially to underserved consumers and businesses that may lack traditional credit histories, it is important to be keenly alert to potential risks around bias and inequitable outcomes. for example, if ai models are built on historical data that reflect racial bias or are optimized to replicate past decisions that may reflect bias, the models may amplify rather than ameliorate racial gaps in access to credit. along those same lines, the opaque and complex data interactions relied upon by ai could result in discrimination by race, or even lead to digital redlining, if not intentionally designed to address this risk. it is our collective responsibility to ensure that as we innovate, we build appropriate guardrails and protections to prevent such bias and ensure that ai is designed to promote equitable outcomes. as rayid ghani notes, “ … [ a ] ny ai ( or otherwise developed ) system that is affecting people ’ s lives has to be explicitly built to focus on increasing equity and not just optimizing for efficiency … [ w ] e need to make sure that we put guidelines in place to maximize the chances of the positive impact while protecting people who have been traditionally marginalized in society and may be affected negatively by the new ai systems. ” 9 federal advisory council ( fac ) record of meeting, ( december 3, 2020 ), https : / / www