Explainable / Interpretable AI

Today we will be discussing ideas around interpretability in AI systems. Please come to class prepared to discuss the readings.

Slides

• Discussion questions

Readings

Required

• Zittrain, Jonathan (2020). Lecture 2: With Great Power Comes Great Ignorance: What’s Wrong When Machine Learning Gets It Right. The Berkman Klein Center for Internet & Society.
• Weld, Daniel & Bansal, Gagan (June 2019). The Challenge of Crafting Intelligible Intelligence. Communications of the ACM, Vol. 62 No. 6, Pages 70-79.

Optional

The following readings are recommended for additional context:

• Rudin, Cynthia (May, 2019). Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead. Nature Machine Intelligence.
• (2018). The Building Blocks of Interpretability. Google Brain + CMU.