In this episode of the Data Show, I spoke with Kartik Hosanagar, professor of technology and digital business, and professor of marketing at The Wharton School of the University of Pennsylvania. Hosanagar is also the author of a newly released book, A Human’s Guide to Machine Intelligence, an interesting tour through the recent evolution of AI applications that draws from his extensive experience at the intersection of business and technology.
We had a great conversation spanning many topics, including:
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- The types of unanticipated consequences of which algorithm designers should be aware.
- The predictability-resilience paradox: as systems become more intelligent and dynamic, they also become more unpredictable, so there are trade-offs algorithms designers must face.
- Managing risk in machine learning: AI application designers need to weigh considerations such as fairness, security, privacy, explainability, safety, and reliability.
- A bill of rights for humans impacted by the growing power and sophistication of algorithms.
- Some best practices for bringing AI into the enterprise.
Related resources:
- “Managing risk in machine learning”: considerations for a world where ML models are becoming mission critical
- Francesca Lazzeri and Jaya Mathew on “Lessons learned while helping enterprises adopt machine learning”
- Jerry Overton on “Teaching and implementing data science and AI in the enterprise”
- Kris Hammond on “Bringing AI into the enterprise”
- Jacob Ward on “How social science research can inform the design of AI systems”
- “Overcoming barriers to AI adoption”
- Sharad Goel and Sam Corbett-Davies on “Why it’s hard to design fair machine learning models”
