Responsible AI at Scale
summary
Azure AI describes a concrete responsible-AI pipeline: a 360° impact assessment that yields design requirements, continuous fairness testing using labeled test sets with metadata (education, region) and intersectional analysis, and mitigation with open-source tools Fairlearn InterpretML ErrorAnalysis. Example implementations: Custom Neural Voice runs on limited access, requires applicant review, a recorded consent phrase validated by speech-to-text plus speaker-recognition, and mandates synthetic-voice disclosure; spatial analysis documents limits (not for real-time safety, not tested on under‑18s) and recommends frame/resolution constraints.
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chapters (10)
- 0:00Introduction to Responsible AI
- 1:04Responsible AI principles and process
- 2:27Assessment with Custom Neural Voice
- 4:58Building fairness into systems
- 7:04Testing fairness across services
- 9:11Tools for fairness assessment
- 10:17Enabling responsible use of models
- 12:05Protecting speakers and listeners
- 14:31Technical controls for voice verification
- 15:13Evolution and continuous improvement
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