Technology Economist Susan Athey Adds DOJ Role to Her Multidimensional Career
After winning awards for technical advances in economic theory and industrial organization, acting as chief economist at Microsoft, conducting original research that combines machine learning with econometric modeling, being pioneers in the new field of technology economics, helping to found and foster Stanford HAIand launching the Golub Capital Social Impact Lab at Stanford, Susan Athey He will now try a new hat as chief economist in the antitrust division of the U.S. Department of Justice (DOJ).
The broad nature of his work and career has earned him respect in multiple academic fields. “Susan is a force of nature. It goes from machine learning to business strategy to technology policy to social impact, producing deep ideas at every moment, ”he says. Jonathan Levinthe Philip H. Knight Professor and Dean of the Stanford Graduate School of Business, where Athey is the Gifted Professor of Technology Economics.
While Athey will continue his appointment to the GSB part-time as he achieves his new governing role, the change in his approach offers an opportunity to reflect on the significant impact he has had throughout his career. and during his stay at Stanford.
A model to follow for HAI
Few researchers better exemplify the multidisciplinary mindset it fosters Stanford HAI than Athey. His propensity to immerse himself in various fields of study dates back to his undergraduate days at Duke University, where he graduated in 1991 with a triple degree in economics, mathematics and computer science.
She became a professor of economics and business at MIT, Harvard University, and then Stanford in 2013, but even within the field of economics, Athey’s interests have been diverse: the 2007, won the prestigious award. John Bates Clark Medal for his contributions to multiple subfields, such as industrial organization, microeconomic theory, and econometrics.
But it was during a leave of absence from academia to serve as Microsoft’s chief economist between 2008 and 2013 that Athey established a striking connection between his passion for economics and the tools of AI and learning. automatic.
They already knew that digitalization and technology platforms would play an important role in the economy, and that search engines were about to become disproportionately important. She also knew that the research community was just beginning to address questions about how to design digital markets and how healthy competition was in those markets, and she was excited to help develop this research.
But once he started working at Microsoft, Athey also discovered something he didn’t expect: the potential of machine learning to address financial problems. The creators of the Bing search engine were conducting experiments in a way that economists only dreamed of. Simultaneously, they were conducting thousands of random A / B tests, asking a large number of “and if” questions to better understand things like what search results should go to the top and how to do auctions to set advertising prices in a search page. In comparison, he says, economists usually do an experiment in a year.
“Microsoft used an artificial intelligence system made up of hundreds of algorithms that worked all together to create a search results page,” he says. “That was something new.”
In the field of economics until then, data mining and machine learning had been pejorative terms for a less advanced form of statistics. “They were seen as a mechanical exercise to separate cats from dogs,” he says. But at Microsoft, Athey saw an opportunity to combine the computational advances of predictive machine learning with statistical theory so that researchers could better understand causal effects not only in business applications like the search engine, but also in the social sciences and economy. It was an epiphany that launched her into a new direction of research and helped define her as one of the first technological economists.
Machine learning and causal effects
From his experience at Microsoft, Athey realized that knowledge of predictive algorithms could be harnessed in new ways by combining them with recent developments in econometrics and statistics. For example, machine learning algorithms could be adapted to answer questions of cause and effect in economics, such as what will happen if we change the minimum wage? Expand immigration policy? Raise prices? Allow two companies to merge? “Predictive machine learning can’t solve these questions on its own, but it can help,” he says.
For example, Athey has used machine learning to analyze the impact on consumers of personalized prices, a form of price discrimination that involves charging different prices to consumers according to their willingness to pay. Traditional economic methods would provide aggregate solutions to this problem, he says. They might study one product category at a time, taking into account the demand for, say, different brands of yogurt or towels. By applying machine learning methods to consumers ’historical purchasing data, Athey’s research group can estimate consumers’ personalized preferences across multiple products at once.
Building these predictive consumer choice models, in turn, allows researchers to ask even bigger questions about things like what happens to prices if you apply a tariff or if generics hit the market. “As a contribution to answering these questions, we want to understand how consumers make decisions,” Athey says. And machine learning offers this input in a way that allows researchers to do this work more efficiently, on a larger scale, and in a more personalized way. “If you assume everyone is the same, that gives different answers than if you assume people have different preferences,” he says.
A pioneer of technological economics
Athey’s position as chief economist at Microsoft ended in 2013, but his tenure there defined her as one of the first people to be considered a “technology economist.” Since then, it is a field that has helped establish itself as an independent discipline by convening early lectures in the field and guiding numerous students throughout this career.
“Technology economists are now holding an annual conference that attracts about 800 participants,” he says. “And we have a specialized job market because being a technology economist is a different profession that people can pursue.”
Athey has it too written about what it means to be a technology economist. “It’s partly a career, but it’s also a combination of different fields of study,” he says. Technology economists study the impact of digitalization on the economy, which involves thinking about market design, privacy, data security, equity, competition policy, and more, he says. “They also help create and analyze business models and competitive strategy, and connect models with data to guide decisions.”
Advancing AI for good
At Microsoft, in addition to taking an unexpected deep dive into machine learning and AI, Athey witnessed first-hand the challenges posed by these technologies: ethical and legal issues, First Amendment issues, equity and bias, privacy, and copyright and the prevalence of unintended consequences. as people manipulated or played the system in response to market changes or new rules.
Because of these observations, Athey developed a desire to influence the ways in which machine learning and AI would develop in the world. When he returned to full-time academy, his first steps in this direction included helping to plan the launch of HAI and then becoming one of the founding associate directors of HAI. “Stanford HAI was really created to address these issues,” he says. “We want AI to be beneficial to humans and we want to avoid all of these unintended consequences.”
Athey also wanted to translate the successful uses of AI from the for-profit sector into the social impact sector. This impulse led her to launch the Golub Capital Social Impact Lab and Stanford. “We’re bringing the set of technology tools to social impact applications,” he says. Thus, for example, the Social Impact Lab has conducted case studies of digital education technology to improve student learning; approaches developed and implemented to guide educational messages to maximize farmer participation; developed and evaluated digital tablet applications that guide nurses through patient counseling; and developed methods to prioritize candidates for clinical trials of drugs for COVID-19.
Connecting the dots to the DOJ
Applying machine learning to interesting social issues at Golub Capital’s Social Impact Lab is a bottom-up approach to making change, Athey says. On the contrary, in his new job as chief economist of the antitrust division in the DOJ, he will try to address the problems of the digital economy from top to bottom. “Government laws and policies affect everything from how competition works to mergers, to the investments people make,” Athey says.
By moving to the DOJ, Athey hopes to continue many of HAI’s efforts to help governments adapt to a rapidly changing era of technology, especially when it comes to the use of data in industry and government. “Because technology like artificial intelligence is moving so fast, it’s hard for the government to keep up,” he says. “We need to find out how all branches of government will be prepared to guide us through a different era.”
It’s a good time for the Athey to try out the government’s work, Levin says. “At a time when technology is on the rise and the promotion of competition is essential, I can’t think of anyone who would rather have the DOJ than Susan.”
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