BERKELEY, Calif. —In 2009, in the midst of the global financial crisis, Paul Volcker, the former Federal Reserve chair, famously observed that the only socially productive financial innovation of the preceding 20 years was the automated teller machine. One wonders what Volcker would make of the tsunami of digitally enabled financial innovations today, from mobile payment platforms to internet banking and peer-to-peer lending.
“ In an old parable about banks and regulators, the banks are greyhounds—they run very fast—while the regulators are bloodhounds, slow afoot but faithfully on the trail. In the age of the platform economy, the bloodhounds are at risk of losing the scent. ” Governments also have laws and regulations to prevent providers of financial products from discriminating on the basis of race, gender, ethnicity, and religion. The challenge here is distinguishing between price discrimination based on group characteristics and price discrimination based on risk.
In algorithmic processes, moreover, the source of bias can vary. The data used to train the algorithm may be biased. Alternatively, the training itself may be biased, with the AI algorithm “learning” to use the data in biased ways. Given the black-box nature of algorithmic processes, the location of the problem is rarely clear.
It is not clear that this is enough. Big Techs can use their platforms to generate large amounts of customer data, employ it in training their AI algorithms, and identify high-quality loans more efficiently than competitors lacking the same information.
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Of course disruption always threatens the existing power structure. The fear word they always use? Stability.
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