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

Service Cards and ML Governance with Michael Kearns - #610

Jan 2, 2023

In a fascinating discussion, Michael Kearns, a UPenn professor and Amazon Scholar, delves into the vital topics of AI governance and fairness. He describes the innovative service cards introduced at Amazon, emphasizing their holistic approach compared to traditional model cards. Kearns also tackles the ongoing debate surrounding algorithmic versus dataset bias, reflecting on current challenges in fairness within large language models. His insights shed light on the importance of responsibly addressing these issues in AI development.

39:08

Creator website

Episode guests

Michael Kearns

AI Chapters

Episode notes

Intro

2min

The Evolution of Machine Learning

7min

Exploring Responsible AI in Amazon's AWS

4min

Introduction to Service Cards in AI Governance

6min

Navigating Demographic Bias Assessments in AI Services

5min

Navigating Bias and Fairness in Machine Learning

17min

Today we conclude our AWS re:Invent 2022 series joined by Michael Kearns, a professor in the department of computer and information science at UPenn, as well as an Amazon Scholar. In our conversation, we briefly explore Michael’s broader research interests in responsible AI and ML governance and his role at Amazon. We then discuss the announcement of service cards, and their take on “model cards” at a holistic, system level as opposed to an individual model level. We walk through the information represented on the cards, as well as explore the decision-making process around specific information being omitted from the cards. We also get Michael’s take on the years-old debate of algorithmic bias vs dataset bias, what some of the current issues are around this topic, and what research he has seen (and hopes to see) addressing issues of “fairness” in large language models.

The complete show notes for this episode can be found at twimlai.com/go/610.

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