Looking at AI Through the Lens of a Chief Economist (Walmart, Uber, Lyft)

The presentation discusses the complementarities between AI and behavioral economics, with examples from the speaker's experiences as Chief Economist at Uber, Lyft, and Walmart. The speaker emphasizes the importance of effect identification, architectural nudges, and detection of heterogeneity in using AI to improve the bottom line and make the world better.

• The speaker shares examples of using AI and behavioral economics to improve products and scaling
• Effect identification, architectural nudges, and detection of heterogeneity are important tools in using AI to improve the bottom line and make the world better
• Examples include the economics of apologies at Uber, pricing and wait times at Lyft, and the voltage effect on scaling
• The speaker emphasizes the importance of combining economic theory, field experiments, and AI to identify causal effects and correlations

One example the speaker shares is the Uber apologies project, which involved using ML to identify customers who had a bad experience and offering them a personalized apology. This led to a significant increase in customer satisfaction and loyalty. The project then evolved into a field experiment where different types of apologies were tested to see which was most effective. The results of the experiment were then used to improve the ML algorithm and create a new product that offered personalized apologies to customers.

John List is a Professor at the University of Chicago and the Chief Economist at Walmart; he formerly served as Chief Economist at Lyft and Uber. Economists look at machine learning through a different lens. They identify causal effects, detect heterogeneity, and generally want to improve the bottom line and make the world better. List will take you through his experiences with machine learning and share the economics of apologies at Uber, the value of time at Lyft, and how the voltage effect impacts machine learning at scale. The Voltage Effect: How to Make Good Ideas Great and Great Ideas Scale is List’s latest best-selling book. To obtain data for the field experiments he pioneered in the 1990s, List has used several different markets, including charitable fundraising activities, the sports trading card industry, the ride-share industry, and the education sector, to highlight a few. This collective research has led to collaborative work with several schools, charities, and businesses, including Humana, United Airlines, Sears, General Motors, and many others. List’s research includes over 200 peer-reviewed journal articles and several published textbooks. He co-authored the international best seller, The Why Axis, in 2013. He is a current editor of The Journal of Political Economy.