Machine learning and society: Bias, fairness, and explainability
summary
Examines how machine learning interacts with society by showing real-world examples of measuring and reducing bias in ML models and improving fairness and explainability. Covers both business and technical considerations for developing ML responsibly.
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chapters (10)
- 0:00Introduction and perspective
- 1:44Advancements in AI technology
- 4:05Benchmarking and performance
- 7:33Limitations of current models
- 10:35AI in a societal context
- 13:36Human failures and bias
- 18:11Improving justice and fairness
- 23:56Bias in machine learning
- 28:24Defining fairness standards
- 33:52Explainability and outlook
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