Ethics and Governance of AI (Fall 2019)

John Bowers (Harvard Law School, Berkman Klein Center)

This reading group will pursue a cross-disciplinary investigation of the development and deployment of the opaque complex adaptive systems that are increasingly in public and private use. We will explore the proliferation of algorithmic decision making, autonomous systems, and machine learning and explanation; the search for balance between regulation and innovation; and the effects of AI on the dissemination of information, along with questions related to individual rights, discrimination, and architectures of control.

3 Day 3: Interpretability and Explainability 3 Day 3: Interpretability and Explainability

10/22

3.1. “The Mythos of Model Interpretability” by Zachary C. Lipton (ArXiv, 2016)

“The Mythos of Model Interpretability” by Zachary C. Lipton (ArXiv, 2016)

3.2. “The Judicial Demand for Explainable Artificial Intelligence” by Ashley Deeks (Columbia Law Review, 2019)

“The Judicial Demand for Explainable Artificial Intelligence” by Ashley Deeks (Columbia Law Review, 2019)

3.3. “Stop Doing Explainable ML” by Cynthia Rudin, Talk from “Statistics at a Crossroads: Challenges and Opportunities in the Data Science Era” (2018)

“Stop Doing Explainable ML” by Cynthia Rudin, Talk from “Statistics at a Crossroads: Challenges and Opportunities in the Data Science Era” (2018)

3.4. "Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead" by Cynthia Rudin (Nature, 2019)

"Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead" by Cynthia Rudin (Nature, 2019)