Building Transparent and Ethical Generative AI Models in High-Stakes Industries
summary
Claims specific mitigation steps for generative AI in healthcare and financial services include risk scoring by criticality and likelihood, concrete use‑case contrasts (customer chatbot vs automated loan underwriting) showing when human oversight is required, and a menu of defenses: hallucination and prompt injection countermeasures (RAG, prompt sanitization, input validation), privacy controls (data masking, strict access policies), and model governance (confidence scores, bias audits, red‑team tests, explainable decision traceability). Examples list which risks are high, what tool or process is used, and when human review remains mandatory.
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chapters (6)
- 0:00Ethical generative AI intro
- 2:50Risk assessment importance
- 5:19Assessing generative AI risks
- 7:48Comparing AI use case risks
- 10:15Mitigation strategies
- 13:30Summary: responsible adoption
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