In a large organization, it’s easy for bias to live in the form of machine learning models, but also from hand-coded rules. This talk will explore the ways that bias can creep into models, the sources of it, implications, and discussions you should have with your team to be aware of it.
Key Takeaways
- Recognizing proxies for bias
- The bias that you can inherit from pre-trained models
- Key things to look for when validating a model to detect bias
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