AI Model Audit and Responsible AI Evaluation
TextData Collection
Audited AI and analytical models for governance, data input, bias risk, explainability, model monitoring, and change controls. Reviewed ethical AI factors including model fairness, transparency, accountability, and bias mitigation practices. Evaluated data governance, lineage, integrity, and lifecycle management as part of responsible AI initiatives.
• Led audit and assurance activities focused on AI models and model risk management frameworks.
• Assessed training data quality, data input sources, and annotated output reliability.
• Provided recommendations for improving input data labeling and bias controls in AI-enabled platforms.
• Supported development of best practices for responsible AI and contributed to AI governance maturity.
Audited AI and analytical models for governance, data input, bias risk, explainability, model monitoring, and change controls. Reviewed ethical AI factors including model fairness, transparency, accountability, and bias mitigation practices. Evaluated data governance, lineage, integrity, and lifecycle management as part of responsible AI initiatives. • Led audit and assurance activities focused on AI models and model risk management frameworks. • Assessed training data quality, data input sources, and annotated output reliability. • Provided recommendations for improving input data labeling and bias controls in AI-enabled platforms. • Supported development of best practices for responsible AI and contributed to AI governance maturity.
2025 - 2025