Ethical Considerations in Predictive Analytics
summary
Covers concrete steps for ethical predictive analytics: prioritize robust real-world test sets, use explainability (feature attribution and attribution maps) and adversarial model criticism, and apply targeted fairness metrics rather than bluntly dropping sensitive features; recommends diverse, representative data collection, hypothesis‑driven development and feature engineering to reduce aggregation bias. Cites practical outcomes — pre‑wave addresses supplier risk ethically while Cambridge Analytica shows legal and reputational failure — and urges actionable developer‑facing AI policies and ethical audits.
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chapters (13)
- 0:00Introduction
- 1:20Ethical considerations
- 3:30Predictive analytics basics
- 5:16Ethics and responsible AI
- 7:22Types of bias
- 11:39Bias in ML pipeline
- 16:37Detecting and mitigating bias
- 19:52Fair models with fairness metrics
- 22:18Privacy and data ownership
- 23:24Challenges with ethical guidelines
- 24:29Ethical case studies
- 25:54Deep learning challenges
- 26:59Key takeaways
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