Diversity, Inclusion and Bias in AI Systems
Most companies are increasingly trying to leverage data science to achieve their business goals, but it's easy to create biased models that disadvantage underrepresented groups. In this episode I get to chat with Labhesh Patel, CTO and Chief Scientist at Jumio.
We start by looking at common types of data bias around representation and features that may be highly correlated with protected features (zip codes that are highly correlated with race, for example) and the challenges of supervised learning where the team tagging your data can also introduce bias into models.
We then discuss the benefits of increasing the diversity and inclusiveness of both your tagging and modeling teams and questions you can ask as an engineering leader to make sure your data science team is being thoughtful about the potential impact of the models they're building.
VIDEOS RELATED TO MANAGING
Neha Agarwal, Director of Engineering at Thumbtack
Dan Langevin, CTO at Vericred, Inc.
Mark Van de wiel, VP of Technology at Fivetran
Ramana Satyavarapu, CTO at Finix
Ron Lichty, Interim VP Eng at Ron Lichty Consulting
Joe Bradley, Chief Scientist, SVP Data Science at LivePerson
Claudius Mbemba, CTO at Spritz (formerly Neu)
Kimberly Wiefling, Cofounder at Silicon Valley Alliances
Mona Soni, CTO at Sustainable1 at S&P
Kevin Goldsmith, CTO at Anaconda
Ann Lewis, Senior Advisor for Technology Delivery at US SBA
Jossie Haines, VP Software Engineering & Head of DE&I at Tile
Copyright © 2023 CTO Connection, All Rights Reserved