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

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