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May 31, 2018
Learning about an ML-Driven Economy
Developments in artificial intelligence (AI) and machine learning (ML) have drawn considerable attention from both the real and financial sides of the economy. The Atlanta Fed's recent Financial Markets Conference, Machines Learning Finance: Will They Change the Game?, explored the implications of AI/ML for the financial system and public policy. The conference also included two macroeconomics-related sessions. A presentation of an academic paper, and the subsequent discussion, looked at why AI/ML has not (yet) shown up in the productivity statistics. Also, a policy panel on the implications of AI/ML developments for monetary policy was part of the conference. This post summarizes the policy panel discussion.
Vincent Reinhart, chief economist at Standish Mellon Asset Management, opened the panel discussion with the observation that developments in AI/ML could affect the performance of the overall economy in a variety of ways. For example, advancing technology could better match workers with jobs and, as a result, boost employment. On the other hand, it could also complicate job matching by forcing jobs and workers to become more specialized.
A combination of three factors is driving the recent growth in AI/ML, explained Carolyn Evans, head economist and senior data scientist at Intel Corporation: increased data availability, faster computers, and improved algorithms for analyzing the data. Like Reinhart, she noted that AI/ML could have various effects on the economy. For example, AI/ML is helping to reduce cost and boost supply. On the demand side, AI/ML is increasing the efficiency of product searches by buyers. However, as some online sellers become better than others at using AI/ML to help customers find the products they want, customer relationships may become stickier. In addition, firms may come to value interactions with customers more highly because these interactions could provide them with valuable data to use with AI/ML to better serve current and future customers. Evans raised the question of whether these developments could change the nature of pricing.
Dallas Fed president Rob Kaplan said he believes AI/ML is causing a structural change. It is not the first new technology to affect the economy, but the economic effects of this technology are more pervasive. For instance, business pricing power is already more constrained than it used to be, but even businesses that seemingly have some power currently worry that they make themselves more vulnerable to AI/ML-enabled disruption if they raise prices. Kaplan also emphasized the importance of skills training and building human capital to alleviate what he views as the inevitable loss of jobs to AI/ML.
The issue of how monetary policymakers should think about AI/ML was the focus of a presentation by Chicago Fed president Charles Evans. He observed that the "sign, magnitude, and timing" of any resulting structural change are all uncertain. This uncertainty, he said, argues against the use of fixed policy rules such as the Taylor Rule. He suggested that the Federal Reserve should instead follow an "outcome-based policy," adjusting policy based on the evolution of expected inflation and unemployment relative to the policy objectives of stable prices and full employment.
You can download the available presentations from the 2018 Financial Markets Conference web pages. The videos will be posted as they become available. Read Notes from the Vault for a summary of sessions on the strengths and weaknesses of ML, some financial regulatory and broader ethical issues, and the use of ML by investors.
- Does Loyalty Pay Off?
- Immigration and Hispanics' Educational Attainment
- Are Tariff Worries Cutting into Business Investment?
- Improving Labor Market Fortunes for Workers with the Least Schooling
- Part-Time Workers Are Less Likely to Get a Pay Raise
- Learning about an ML-Driven Economy
- Hitting a Cyclical High: The Wage Growth Premium from Changing Jobs
- Thoughts on a Long-Run Monetary Policy Framework, Part 4: Flexible Price-Level Targeting in the Big Picture
- Thoughts on a Long-Run Monetary Policy Framework, Part 3: An Example of Flexible Price-Level Targeting
- Thoughts on a Long-Run Monetary Policy Framework, Part 2: The Principle of Bounded Nominal Uncertainty
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