Data Discrimination
summary
Examines how biases and prejudices in society become embedded in datasets and thus influence AI decision-making, using examples like COMPAS and PredPol. Discusses the importance of data quality and awareness of discrimination when building and evaluating machine learning models.
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chapters (8)
- 0:00Data discrimination intro
- 1:10Real algorithmic bias cases
- 6:24Origins of data bias
- 7:30Bias types in datasets
- 11:41Ethical solutions frameworks
- 18:18Synthetic data transparency
- 20:25AI reflects human limits
- 22:34Q&A
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