Model Evaluation & Audit Frameworks: conduct audits on the model lifecycle from training through deployment and monitoring, ensuring compliance with quality, performance, fairness, and risk‑management standards.
Risk Identification & Mitigation: identify model vulnerabilities including bias, fairness violations, harmful hallucinations, security risks, and recommend remediation strategies.
Measurement Metrics & Statistical Validation: define and assess model performance metrics (accuracy, precision/recall, F1, calibration, robustness, fairness metrics), measurement of hallucination rates in LLMs, bias/fairness quantification, confidence scoring, and stability analyses.
Communication & Collaboration: develop and maintain collaborative relationships with stakeholders, including data partners and owners across...