Interview with Don Moore: Algorithms, Overconfidence, and the Future of Decision-making

Dr. Don Moore is the Lorraine Tyson Mitchell Chair in Leadership and Communication at UC Berkeley Haas School of Business.
His research interests include overconfidence—including when people think they are better than they actually are, when people think they are better than others, and when they are too sure they know the truth.

“Algorithms force us to confront the details of decisions in ways that vague human judgments can sort of skirt around, and in that way algorithms challenge us to get serious about our decision-making by being clear.”

Jump to a question:

• Who are you, where do you work, and what is the primary focus of your research?

• What do people think about algorithmic judgment?

• What do experts think about algorithmic judgment?

• What accounts for the unique suspicion experts harbor toward algorithmic judgment?

• What’s the origin story of your paper on “Algorithm Appreciation?”

• Is people’s trust in algorithms more brittle, or just more suspicious overall?

• What did end up learning about people’s trust in algorithms? Was it impacted by recent advances in artificial intelligence?

• In what cases and for what problems is it appropriate to defer to algorithmic judgment?

• Do people’s folk theories about how algorithms work impact their willingness to trust them?

• Are human-machine teams the future of decision-making?

• How does machine bias differ from human bias in the case of racial discrimination?

• How should we trade off between procedural fairness and equality of outcome in judging algorithmic bias?

• Will we become overconfident or lose our decision-making abilities if we cede more territory to algorithms?

• What advice would you give decision-makers of the future?