Why AI for Good Fails and How Leaders Can Fix It

Even well-designed algorithms can fail and end up hurting the people they're meant to help. In this episode, Dr. Ben Green, Assistant Professor of Information and Public Policy at the University of Michigan, explains what it takes to build AI that works for humans. He shares what keeps data from being used for good, why AI regulation can't be left to AI companies, and what organizations risk losing when they hand too much of their thinking to AI.

Key Moments:

03:40
The Hidden Cost of Outsourced Thinking
Students using AI ace their homework but fall behind on exams. Ben explains why powerful AI doesn't always make people better at their jobs.
06:31
Why Algorithms Belong in the Loop
Ben argues "human in the loop" sidelines people. Flipping it to "algorithm in the loop" keeps AI in a support role for human decision-makers.
08:09
Finding the Real Problem at Boston EMS
Brought in to cut ambulance response times, Ben found the real driver was homelessness and mental health calls, which led to a new specialized team.
16:45
The Data Scientist's Most Underrated Skill
Asking questions builds trust with stakeholders and forces the humility needed to understand a problem before solving it.
24:48
Why AI Companies Can't Police Themselves
Ben calls voluntary AI safety pledges a PR move and argues for legal liability, pre-deployment testing, and a new direction for AI innovation.

Key Quotes: 

Mentions: 

• NEW: Algorithmic Realism by Dr. Ben Green
• Seeing like a State by James C. Scott
• The Public is Getting Fed Up With Data Centers. Politicians Need to Take Notice

Guest Bio: 

Ben Green is an Assistant Professor of Information and Public Policy at the University of Michigan. He is also a Faculty Associate at the Berkman Klein Center for Internet & Society at Harvard. Ben holds a PhD in Applied Mathematics from Harvard University, with a secondary field in Science, Technology, and Society.

Through his research, writing, and advocacy, Ben contributes practical lessons for achieving a world in which AI improves society rather than exacerbating inequality and oppression. He is the author of two books from the MIT Press: just released Algorithmic Realism: Data Science Practices to Promote Social Justice (2026) and The Smart Enough City: Putting Technology in Its Place to Reclaim Our Urban Future (2019). 

Ben regularly engages policymakers and the public to promote equitable technology adoption and regulation. He has published op-eds in outlets including The New York Times, The Wall Street Journal, and The Hill, and has been quoted in newspapers such as The New York Times, The Washington Post, and the Los Angeles Times.