38. Böhm-Bawerk Lecture

The Use of Algorithms in Society

Cass R. Sunstein is currently the Robert Walmsley University Professor at Harvard. He is the founder and director of the Program on Behavioral Economics and Public Policy at Harvard Law School. In 2018, he received the Holberg Prize from the government of Norway, sometimes described as the equivalent of the Nobel Prize for law and the humanities. In 2020, the World Health Organization appointed him as Chair of its technical advisory group on Behavioural Insights and Sciences for Health. From 2009 to 2012, he was Administrator of the White House Office of Information and Regulatory Affairs, and after that, he served on the President’s Review Board on Intelligence and Communications Technologies and on the Pentagon’s Defense Innovation Board. Mr. Sunstein has testified before congressional committees on many subjects, and he has advised officials at the United Nations, the European Commission, the World Bank, and many nations on issues of law and public policy. He serves as an adviser to the Behavioural Insights Team in the United Kingdom.
Mr. Sunstein is author of hundreds of articles and dozens of books, including Nudge: Improving Decisions about Health, Wealth, and Happiness (with Richard H. Thaler, 2008), Simpler: The Future of Government (2013), The Ethics of Influence (2015), #Republic (2017), Impeachment: A Citizen’s Guide (2017), The Cost-Benefit Revolution (2018), On Freedom (2019), Conformity (2019), How Change Happens (2019), and Too Much Information (2020). He is now working on a variety of projects involving the regulatory state, “sludge” (defined to include paperwork and similar burdens), fake news, and freedom of speech.

Abstract:
The judgments of human beings can be biased; they can also be noisy. Across a wide range of settings, use of algorithms is likely to improve accuracy, because algorithms will reduce both bias and noise. Indeed, algorithms can help identify the role of human biases; they might even identify biases that have not been named before. As compared to algorithms, for example, human judges, deciding whether to give bail to criminal defendants, show Current Offense Bias and Mugshot Bias; as compared to algorithms, human doctors, deciding whether to test people for heart attacks, show Current Symptom Bias and Demographic Bias. But in important cases, algorithms struggle to make accurate predictions, not because they are algorithms but because they do not have necessary data. (1) Algorithms might not be able to identify people’s preferences, which might be concealed or falsified, and which might be revealed at an unexpected time. (2) Algorithms might not be able to foresee the effects of social interactions, which can lead in unanticipated and unpredictable directions. (3) Algorithms might not be able to anticipate sudden or unprecedented leaps or shocks (a technological breakthrough, a successful terrorist attack, a pandemic, a black swan). (4) Algorithms might not have “local knowledge,” or private information, which human beings might have. (5) Algorithms might not be able to foresee the effects of context, timing, serendipity, or mood. Predictions about romantic attraction, about the success of cultural products, and about coming revolutions are cases in point. The limitations of algorithms are analogous to the limitations of planners, emphasized by Hayek in his famous critique of central planning. It is an unresolved question whether and to what extent some of the limitations of algorithms might be reduced or overcome over time, with more data or various improvements; in the relevant contexts, there is no equivalent to the price system to elicit and aggregate dispersed knowledge.