Scaling and democratizing AI at Facebook and understanding fairness and algorithmic bias.

Joaquin Quiñonero Candela is the Tech Lead for Responsible AI, a Director of Engineering in AI at Facebook where he built the Applied Machine Learning team which powers all production applications of AI across Facebook’s products.

Prior to this, Joaquin taught at the University of Cambridge, and worked at Microsoft Research.

Reference papers from Timnit Gebru:
- https://arxiv.org/pdf/1908.06165.pdf
- https://arxiv.org/abs/1912.10389]
- http://proceedings.mlr.press/v81/buolamwini18a.html

Topics Covered:
0:00 Defining fairness
0:22 Intro + Bio
0:53 Looking back at building and scaling AI at Facebook
10:31 How do you ship a model every week?
15:36 Getting buy in to use a system
19:36 More on ML tools
24:01 Responsible AI at facebook
38.33 How to engage with those effected by ML decisions
41:54 Approaches to fairness
53:10 How to know things are built right
59:34 Diversity, inclusion, and AI
1:14:21 Underrated aspect of AI
1:16:43 hardest thing when putting models into production

Visit our podcasts homepage for transcripts and more episodes!
www.wandb.com/podcast

Get our podcast on Apple, Spotify, and Google!

Apple Podcasts: https://bit.ly/2WdrUvI
Spotify: https://bit.ly/2SqtadF
Google:http://tiny.cc/GD_Google

We started Weights and Biases to build tools for Machine Learning practitioners because we care a lot about the impact that Machine Learning can have in the world and we love working in the trenches with the people building these models. One of the most fun things about these building tools has been the conversations with these ML practitioners and learning about the interesting things they’re working on. This process has been so fun that we wanted to open it up to the world in the form of our new podcast called Gradient Dissent. We hope you have as much fun listening to it as we had making it!

Join our bi-weekly virtual salon and listen to industry leaders and researchers in machine learning share their research:
http://tiny.cc/wb-salon

Join our community of ML practitioners where we host AMA's, share interesting projects and meet other people working in Deep Learning:
http://bit.ly/wb-slack

Our gallery features curated machine learning reports by researchers exploring deep learning techniques, Kagglers showcasing winning models, and industry leaders sharing best practices.
https://app.wandb.ai/gallery