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Harvard Business Law Review




Recent advances in the field of artificial intelligence have revived long-standing debates about what happens when robots become smarter than humans. Will they destroy us? Will they put us all out of work? Will they lead to a world of techno-savvy haves and techno-ignorant have-nots? These debates have found particular resonance in finance, where computers already play a dominant role. High-frequency traders, quant hedge funds, and robo-advisors all represent, to a greater or lesser degree, real-world instantiations of the impact that artificial intelligence is having on the field. This Article will argue that the primary danger of artificial intelligence in finance is not so much that it will surpass human intelligence, but rather that it will exacerbate human error. It will do so in three ways. First, because current artificial intelligence techniques rely heavily on identifying patterns in historical data, use of these techniques will lead to results that perpetuate the status quo (a status quo that exhibits all the features and failings of the data itself). Second, because some of the most “accurate” artificial intelligence strategies are the least transparent or explainable ones, decisionmakers may well give more weight to the results of these algorithms than they are due. Finally, because much of the financial industry depends not just on predicting what will happen in the world, but also on predicting what other people will predict will happen in the world, it is likely that small errors in applying artificial intelligence (either in data, programming, or execution) will have outsized effects on markets. This is not to say that artificial intelligence has no place in the financial industry, or even that it is bad for the industry. It clearly is here to stay, and, what is more, has much to offer in terms of efficiency, speed, and cost. But as governments and regulators begin to take stock of the technology, it is worthwhile considering artificial intelligence’s real-world limitations.

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Harvard Law School

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