Monitoring and Evaluation of AI Governance, Responsible AI & ISO 42001 Training Course

Most institutions that take AI seriously now have some governance in place: an AI policy, a register of systems, an approval committee, perhaps a plan for ISO/IEC 42001 certification. The harder question is whether any of it works. A management system that is never measured tends to decay into paperwork, while models in production keep changing as data, users and vendors change. ISO/IEC 42001 requires organisations to decide what needs to be monitored and measured, how results will be analysed and evaluated, and how internal audit and management review will drive improvement, and the EU AI Act places post-market monitoring duties on providers of high-risk systems.

The Monitoring and Evaluation of AI Governance, Responsible AI & ISO 42001 Training Course brings two disciplines together. From monitoring and evaluation practice it takes results frameworks, indicator design, baselines, data quality assessment, evaluation criteria and learning loops. From AI assurance it takes model performance monitoring, drift detection, the measurement of bias and explainability, generative AI evaluation, audit and control testing. The first five days build the measurement framework and the core metrics. Days six to ten go deeper through applied monitoring labs, generative AI and supplier monitoring, a full internal audit exercise, programme evaluation design and an integrated project.

Participants work throughout with a fictional but realistic portfolio of AI systems, including a credit scoring model, a recruitment screening tool and a customer service chatbot, and finish with a complete monitoring and evaluation plan and a working dashboard.

Participants completing the programme will be able to:

• Construct a theory of change and results framework for an AI governance or responsible AI programme.
• Define key performance, risk and control indicators aligned with ISO/IEC 42001 clause 9 and the NIST AI RMF.
• Monitor production models for performance decay, data drift and concept drift using appropriate statistical tests.
• Measure fairness, explainability and the effectiveness of human oversight with recognised metrics, stating their limitations.
• Evaluate generative AI and third-party model behaviour through structured test sets, red-team findings and supplier reporting.
• Plan and conduct an internal audit of an AI management system using ISO 19011 principles.
• Design an evaluation of a responsible AI programme using the OECD-DAC criteria and mixed methods.
• Report findings through dashboards and management review inputs that lead to corrective action.

The course suits professionals who produce or review evidence about AI systems and how they are governed, such as:

• AI governance, responsible AI and AI assurance officers
• Model risk management and model validation teams in banks and insurers
• Internal auditors and IT auditors adding AI to their audit universe
• Monitoring, evaluation and learning specialists in programmes that use or fund AI
• Data scientists and machine learning engineers who own production monitoring
• Compliance, data protection and enterprise risk officers
• ISO management system coordinators preparing for ISO/IEC 42001 audits
• Supervisors and inspectors in regulatory agencies that oversee AI use
• Digital programme officers in development agencies and foundations

By the final day, each participant has produced:

• A theory of change and results framework for a responsible AI programme.
• An indicator reference sheet with definitions, formulas, data sources, frequency and thresholds.
• A working Python notebook that computes drift and fairness metrics on a sample model.
• A generative AI evaluation checklist covering accuracy, groundedness, harmful output and prompt injection.
• An internal audit plan, checklist and sample findings report for an AI management system.
• An evaluation design matrix linking questions, criteria, methods and evidence sources.
• A Power BI dashboard prototype that tracks AI governance performance.
• A complete monitoring and evaluation plan ready to adapt at work.

The ten days alternate between measurement design and hands-on analysis. Teaching methods include:

• A running case portfolio of three AI systems that participants measure, audit and evaluate throughout
• Guided Python labs with prepared notebooks, pandas, Fairlearn and Evidently, supported for those new to coding
• Indicator design clinics where participants critique each other's metrics
• A simulated internal audit with document review and role-played interviews
• Evaluation design workshops drawing on MEAL practice in development and public sector programmes
• A capstone plan and dashboard presented for peer and facilitator feedback

Participants who attend the full programme, complete the monitoring labs and present their capstone plan are awarded a CPD-accredited Certificate of Completion issued by Vision Reach Global Consultancy.

The certificate lists the course title, dates and delivery mode. It confirms professional development rather than an auditing credential, so it does not stand in for ISO/IEC 42001 lead auditor certification.