On March 10, 2022, the Investor Advisory Committee (“IAC”) of the SEC held a morning panel that discussed the ethical issues and fiduciary responsibilities relating to the use of artificial intelligence (“AI”) in the development and deployment of “roboadvisers to provide investment recommendations. Generally, a “roboadviser” is an asset management service that uses portfolio management algorithms or artificial intelligence to provide investment advice to customers, thereby facilitating lower costs, account minimums, and fees as compared to services provided by human investment advisors.11 The panel provided updates on the current use of AI in roboadvising, discussed the technical differences and tradeoffs between AI-powered advice versus recommendations provided by a human advisor, examined how roboadvisers may be subject to bias and blind-spots, and reviewed new developments in the larger-related industry.
One of the panelists noted that while the benefits of AI are being recognized at an expedited pace, there are also risks to using AI in the financial sector. There is a lack of clear “guardrails and standards” to guide AI adoption, which may result in decades of progress being erased in a few lines of code. To prevent this, she noted that the SEC could be helpful in establishing norms and expectations for routine AI testing. The panelist also outlined steps that corporate leadership could take to reduce their potential liability and enhance the benefit of the AI systems they are using; these included, establishing an AI governance framework, identifying the designated point of contact in the C-suite responsible for AI governance, communicating stages of the AI lifecycle where testing will be conducted, documenting relevant findings at the completion of each stage, and implementing routine auditing of AI technology used by the company.
Another way to mitigate bias is to develop new algorithms and models from an “explainable AI” perspective. Before roboadvisers used AI, human advisors applied advanced analytics and statistics to understand customer behavior, which can be tangibly understood and explained, whereas, a machine learning AI uses a “black box,” making it difficult or impossible to understand the basis of the AI’s decisions. The new “explainable AI” movement in the industry seeks to mitigate this issue with new sets of models and algorithms fundamentally designed to be explainable, like a new type of AI model called an “explainable boosted machine.” Additionally, there are two different ways AI can be used: to augment human decision making or to fully replace a human decision maker. One of the panelists advised that keeping a human in the loop provides a check and balance on the results of an AI model, reducing the risk of failure and impactical outcomes. Conversely, in cases where an AI replaces a human decision maker entirely, there is usually a greater need for scrutiny. Since machine-learning products can have an error rate expressed as a percentage, companies must decide at what threshold they can offer the roboadviser for the product. Given the increasing use of AI to develop financial products and to render financial advice, we can expect to see continued debate on topics related to governance and risk controls.
11 See http://schueffel.biz/wp-content/uploads/2017/09/Schueffel-2017-The-Concise-FINTECH-COMPENDIUM.pdf
Originally published in REVERSEinquiries: Volume 5, Issue 2.
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