How to avoid bias in Machine Learning

Machine learning algorithms can have a big blindspot: bias. But where does it come from?
In today’s video in my Code to Care series, I’m exploring the three primary areas of the AI process to look for bias - the data, the model, and the net effect of deploying an AI model. I also cover some relatively famous examples of bias within these three areas.
Bias is a big risk in AI, so I’m exploring where this bias comes from and what you need to evaluate before deploying a new ML model.
If you have any specific questions, drop them in the comments. I’m enjoying your input and I would love to hear from you.
#AI #artificialintelligence #ML #machinelearning #CodetoCare
Check out my LinkedIn: / donwoodlock
0:00 - 0:45 - Introduction: Bias in Machine Learning
0:45 - 2:15 - Exploring the Three Main Areas of AI Bias
2:15 - 5:15 - Data Bias: Identifying and Mitigating Risks
5:15 - 7:30 - Model Bias: Understanding and Correcting
7:30 - 10:00 - Deployment: Evaluating the Net Effects of Bias in AI
10:00 - 12:30 - Real-World Examples: Bias in Action
12:30 - 14:15 - Strategies for Identifying and Countering Bias
14:15 - 14:45 - Conclusion: Final Thoughts on Mitigating Bias in AI
---
ABOUT INTERSYSTEMS
Established in 1978, InterSystems Corporation is the leading provider of data technology for extremely critical data in healthcare, finance, and logistics. It’s cloud-first data platforms solve interoperability, speed, and scalability problems for large organizations around the globe.
InterSystems Corporation is ranked by Gartner, KLAs, Forrester and other industry analysts as the global leader in Data Access and Interoperability. InterSystems is the global market leader in Healthcare and Financial Services.
Website: www.intersystems.com/
CZcams: / @intersystemscorp
LinkedIn: / intersystems
Twitter: / intersystems

[@user-pf7sw4ey1u]

Thank you sooo much for all the amazing content!

[@peterbedford2610]

I really like your "use cases" examples. Thanks

[@MichaelRuddock]

These videos are excellent, and so clearly explained. thank you very much.

[@siddharthvj1]

nice