In the first of a special mini-series focused on AI in the workplace, David Creelman and I consider how AI is impacting the workplace. Joining us on the HRchat podcast is Avi Goldfarb, the Rotman Chair in Artificial Intelligence and Healthcare and a professor of marketing at the Rotman School of Management, University of Toronto.
Avi is also Chief Data Scientist at the Creative Destruction Lab, a faculty affiliate at the Vector Institute and the Schwartz-Reisman Institute for Technology and Society, and a Research Associate at the National Bureau of Economic Research.
Avi’s research focuses on the opportunities and challenges of the digital economy. Along with Ajay Agrawal and Joshua Gans, Avi is the author of the bestselling books Prediction Machines and Power and Prediction.
He has published academic articles in marketing, statistics, law, management, medicine, political science, refugee studies, physics, computing, and economics. Avi has testified before the U.S. Senate Judiciary Committee on related work in competition and privacy in digital advertising. His work has been referenced in White House reports, European Commission documents, the New York Times, the Economist, and elsewhere.
Questions for Avi include:
- You have been studying AI for a long time; well before its advances garnered the attention of the general population. Is anything taking you by surprise with the current advances?
- You have talked about AI automating prediction in ways that we had only understood humans could do well/reasonably well previously. Can you explain this for our audience?
- You have an interesting (and hopeful) perspective that AI may become more of an equalizer across professions than past advances have been, can you tell us more about that?
- Can you provide your thoughts on how long it will be until we see broad sweeping changes in work and how it is organized?
- With your extensive work with entrepreneurs on the bleeding edge of new technology, what advice do you have for those on the other side of the equation: those who are making recommendations and purchasing tech for their organization. How should we balance not being left behind with appropriate diligence?