Solon Barocas's guest appearances

Solon Barocas

The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

Center for Internet and Society

Good Code

Brave New World -- hosted by Vasant Dhar

Peter Lee

Luke Stark

Nick Bostrom

Ellen Goodman

Helen Nissenbaum

Julia Powles

Peter Asaro

Kevin Kelly

Dennis Mortensen

Rob Walker

Sanmi Koyejo

Chelsea Finn

Dario Amodei

Jianxiong Xiao

Kate Darling

OwlTail

In this episode, we speak with Solon Barocas, an Assistant Professor in Information Science at Cornell University. He is currently on leave at Microsoft Research in New York. But he will be returning to Cornell to teach. We ask him about a class on ethics in data science he has designed for aspiring computer scientists. Similar classes are burgeoning all around the country. How do you best infuse a culture of ethics in the field? What more do young professionals need to assess the social impact of their work? It’s important to make sure that students know to reject certain applications of machine learning, Barocas says. But it's not enough to then expect them to go and change the world, he warns.

In this episode, we speak with Solon Barocas, an Assistant Professor in Information Science at Cornell University. He is currently on leave at Microsoft Research in New York. But he will be returning to Cornell to teach. We ask him about a class on ethics in data science he has designed for aspiring computer scientists. Similar classes are burgeoning all around the country. How do you best infuse a culture of ethics in the field? What more do young professionals need to assess the social impact of their work? It’s important to make sure that students know to reject certain applications of machine learning, Barocas says. But it's not enough to then expect them to go and change the world, he warns.

Solon Barocas on Teaching Ethics in Data Science

Today we’re joined by Solon Barocas, Assistant Professor of Information Science at Cornell University.Solon and I caught up to discuss his work on model interpretability and the legal and policy implications of the use of machine learning models. In our conversation, we explore the gap between law, policy, and ML, and how to build the bridge between them, including formalizing ethical frameworks for machine learning. We also look at his paper ”The Intuitive Appeal of Explainable Machines.”

Today we’re joined by Solon Barocas, Assistant Professor of Information Science at Cornell University. Solon and I caught up to discuss his work on model interpretability and the legal and policy implications of the use of machine learning models. In our conversation, we explore the gap between law, policy, and ML, and how to build the bridge between them, including formalizing ethical frameworks for machine learning. We also look at his paper ”The Intuitive Appeal of Explainable Machines.”

Legal and Policy Implications of Model Interpretability with Solon Barocas - TWiML Talk #219

Happy new year! I'm pleased to post the first show of the winter quarter, Show # 227, January 14, 2015, my interview with Solon Barocas, Postdoctoral Research Associate at Princeton's Center for Information Technology Policy, co-author of the article Big Data's Disparate Impact (with Andrew D. Selbst). Algorithmic computing and decision-making have entered our world much faster than our understanding of it. In Solon's article, he takes a close look at the massively under-explored impact of algorithms on traditional forms of employment discrimination under Title VII of the Civil Rights Act (think discrimination on the basis of race or gender). Identifying both the technical and legal issues involved is a challenge, but this article does a wonderful job exposing the risks of algorithms in this space, which often (although not exclusively) includes embedding human prejudices in the code itself. We examined these and other ramifications of algorithmic computing and civil rights discrimination in our discussion. I greatly enjoyed it (recorded at Princeton!) and hope that you find it illuminating.{Hearsay Culture is a talk show on KZSU-FM, Stanford, 90.1 FM, hosted by Center for Internet &amp; Society Resident Fellow David S. Levine. The show includes guests and focuses on the intersection of technology and society. How is our world impacted by the great technological changes taking place? Each week, a different sphere is explored. For more information, please go to http://hearsayculture.com.}

Happy new year! I'm pleased to post the first show of the winter quarter, Show # 227, January 14, 2015, my interview with Solon Barocas, Postdoctoral Research Associate at Princeton's Center for Information Technology Policy, co-author of the article Big Data's Disparate Impact (with Andrew D. Selbst). Algorithmic computing and decision-making have entered our world much faster than our understanding of it. In Solon's article, he takes a close look at the massively under-explored impact of algorithms on traditional forms of employment discrimination under Title VII of the Civil Rights Act (think discrimination on the basis of race or gender). Identifying both the technical and legal issues involved is a challenge, but this article does a wonderful job exposing the risks of algorithms in this space, which often (although not exclusively) includes embedding human prejudices in the code itself. We examined these and other ramifications of algorithmic computing and civil rights discrimination in our discussion. I greatly enjoyed it (recorded at Princeton!) and hope that you find it illuminating. {Hearsay Culture is a talk show on KZSU-FM, Stanford, 90.1 FM, hosted by Center for Internet &amp; Society Resident Fellow David S. Levine. The show includes guests and focuses on the intersection of technology and society. How is our world impacted by the great technological changes taking place? Each week, a different sphere is explored. For more information, please go to <a href="http://hearsayculture.com" rel="nofollow" target="_blank">http://hearsayculture.com</a>.}

Solon Barocas - Hearsay Culture Show #227 - KZSU-FM (Stanford)