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:

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”