Decoding the algorithm - why explainability and transparency matter when buildin...
summary
Explains why algorithmic decisions matter for high‑stakes use cases and what was built: a machine‑marking English assessment with human-in-the-loop controls, ethics and legal review, and continuous QA to avoid bias. Cites concrete risks (ETS case where ~15,000 visas were revoked) and practical tools for interpretability such as LIME and SHAP plus saliency maps, argues for simpler models when adequate and for tailoring explanations to user role and urgency.
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chapters (15)
- 0:00Explainability and transparency
- 1:05Existential risks of unaligned AI
- 6:21Language assessment decisions
- 8:06Algorithmic decision-making challenges
- 10:34Algorithmic power spectrum
- 17:16Defining AI and its impact
- 20:31Why explainability matters
- 23:23AI explainability laws
- 29:05Designing explainable systems
- 33:17Building explainability in
- 35:05Rationalizing algorithmic outputs
- 40:45Explainability benefits
- 41:26Ethical assessment case study
- 44:37AI deployment takeaways
- 46:02Summary and closing
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