Machine Learning Experts - Margaret Mitchell

Hey friends! Welcome to Machine Learning Experts. I'm your host, Britney Muller and today’s guest is none other than Margaret Mitchell (Meg for short). Meg founded & co-led Google’s Ethical AI Group, is a pioneer in the field of Machine Learning, has published over 50 papers, and is a leading researcher in Ethical AI.

You’ll hear Meg talk about the moment she realized the importance of ethical AI (an incredible story!), how ML teams can be more aware of harmful data bias, and the power (and performance) benefits of inclusion and diversity in ML.

Very excited to introduce this powerful episode to you! Here’s my conversation with Meg Mitchell:

Transcription:

Note: Transcription has been slightly modified/reformatted to deliver the highest-quality reading experience.

Could you share a little bit about your background and what brought you to Hugging Face?

Dr. Margaret Mitchell’s Background:

• Bachelor’s in Linguistics at Reed College - Worked on NLP
• Worked on assistive and augmentative technology after her Bachelor’s and also during her graduate studies
• Master’s in Computational Linguistics at the University of Washington
• PhD in Computer Science

Meg: I did heavy statistical work as a postdoc at Johns Hopkins and then went to Microsoft Research where I continued doing vision to language generation that led to working on an app for people who are blind to navigate the world a bit easier called Seeing AI.

After a few years at Microsoft, I left to work at Google to focus on big data problems inherent in deep learning. That’s where I started focusing on things like fairness, rigorous evaluation for different kinds of issues, and bias. While at Google, I founded and co-led the Ethical AI Team which focuses on inclusion and transparency.

After four years at Google, I came over to Hugging Face where I was able to jump in and focus on coding. I’m helping to create protocols for ethical AI research, inclusive hiring, systems, and setting up a good culture here at Hugging Face.

For example: