Addressing Privacy, Bias, and Fairness in AI Systems
summary
Concrete claims and outcomes guide decisions: systems use differential privacy (ε≈2–4 at Apple, ≈0.3 at Google) and federated learning plus GAN synthetic data, a recommendation engine shows women receive suggestions 18% less aligned, a healthcare pipeline raises non‑white representation from 24.7% to 49% and cuts performance disparity by 68%, documentation records lineage and trade‑offs, and tools like IBM AI Fairness 360 and Microsofts fair appear useful but require production validation.
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chapters (12)
- 0:00Introduction to Ethical Data Engineering
- 0:47The Era of Exponential Data Growth
- 1:05Case Studies: Data Failures and Bias
- 3:03Privacy-Preserving Techniques
- 4:03Addressing Algorithmic Bias
- 4:55Case Studies: Real-World Applications
- 6:39Implementing Ethical Gates
- 7:20Documentation and Transparency
- 8:11Interdisciplinary Collaboration
- 8:54Automating Ethical Checks
- 9:42Regulations and Compliance
- 10:16Conclusion: Building Trustworthy Systems
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