The Curious Case of Tay: Limitations of AI in Wealth Management

The Curious Case of Tay: Limitations of AI in Wealth Management

The American College of Financial Services
February 1, 2021

When considering the impact of artificial intelligence and AI-based programs on wealth management from both client-facing and back office perspectives, it's important to note that while AIs have intellectual capabilities humans may not, they are far from perfect. Just as a financial advisor can make a bad decision after getting bad advice, AIs depend on the quality of their input to provide quality answers: something every financial planning professional needs to consider.

Take, for example, the case of TAY.

In 2016, computer giant Microsoft ran an experiment to see if an artificial intelligence program could integrate successfully into conversations with humans online. The program, called "Thinking About You" or TAY for short, was designed to mimic the mannerisms and language of a typical 19-year-old girl and was released onto Twitter to see how it would react.

TAY's tweets started out innocently enough as it gave computer-generated responses to other Twitter users, but it didn't last: along with everything else, TAY was exposed to the darkest parts of the Internet including trolls and their politically incorrect and inflammatory language. Before long, TAY was spewing vile obscenities and racist, sexist rhetoric across the Twitter community, and after a grand total of 16 hours online, TAY was shut down and Microsoft issued a public apology for its behavior.

TAY's breakdown was well covered in the media, as experts pointed out Microsoft had given TAY the guideline of deliberately copying the language patterns of other users to sound more human without defining what topics were acceptable for conversation. It followed a similar issue with IBM's supercomputer Watson, which had to be given a profanity filter after it assimilated the website UrbanDictionary into its program in 2013.



The problem with the AIs was bad input exacerbated by their "narrow" nature: unlike a human who can be a brilliant financial advisor while also being able to beat their friends in poker, take out the trash, fold the laundry, and determine what to say and what not to say in conversation, most AIs in use today can only do the one thing they are designed to do. Even in this, they need guidance from humans to establish a baseline of proper behavior, and if that guidance is in any way biased or prejudiced, the AI itself will reflect it.

Issues with discrimination in AI continue to raise their ugly head in the form of common Criminal Risk Assessment Algorithms (CRAAs) that use historical crime data to inform judges' sentencing decisions: many experts agree that, based on long-standing patterns of law enforcement bias that disproportionately affect minorities, CRAAs tend to judge offenders of color more harshly than similar white offenders.

In the context of wealth management strategies, AI bias presents a similar problem. Many in the financial services industry see female clients as a large part of the financial future. If a firm of investors is looking to pivot to serve these clients, but are only feeding their historically male-majority portfolio outcomes into an AI, that AI's decision-making can produce misleading conclusions and give bad data to the financial professional trying to understand a new market.




AI: A Long Term Commitment to Responsibility


When we examine what AI can do for financial advisors, it's important to understand the limitations of technology as well as its potential. Because AI requires the best resources to give the best results, it also requires a shared commitment from wealth management professionals to be the best versions of themselves. Only by recognizing potential sources of bias within ourselves and our historical methods of doing business can we successfully transition into the financial future.

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