Policies, ethics committees and model approval forms say little about whether AI is actually under control. Models drift as data changes, fairness gaps reopen after retraining, vendors update foundation models without warning and oversight steps quietly get skipped. Without reliable evidence, boards and regulators cannot tell a functioning governance system from a paper one. Across ten days you learn to measure it. You build results frameworks and theories of change for responsible AI, then define key performance, risk and control indicators mapped to ISO/IEC 42001 clause 9, the NIST AI RMF measure function and ISO/IEC 23894.
Technical sessions cover data and concept drift, the population stability index, fairness metrics such as demographic parity and equalised odds, explainability checks and the evaluation of large language model outputs, with hands-on labs in Python using Fairlearn and Evidently. Later days turn to internal audit under ISO 19011, control effectiveness testing, programme evaluation using the OECD-DAC criteria, management review and dashboards in Power BI. It suits AI assurance and model risk staff, internal auditors, M&E specialists, data scientists, compliance officers and supervisors. Classroom, online and in-house formats lead to a CPD-accredited certificate.
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:
By the final day, each participant has produced:
The ten days alternate between measurement design and hands-on analysis. Teaching methods include:
Day 1: Why AI Governance Must Be Measured
Day 2: Results Frameworks and Theories of Change for Responsible AI
Day 3: Designing Indicators for an AI Management System
Day 4: Monitoring Models in Production
Day 5: Measuring Fairness, Transparency and Human Oversight
Day 6: Applied Monitoring Lab: Drift and Fairness in Python
Day 7: Monitoring Generative AI and Third-Party Models
Day 8: Internal Audit and Control Effectiveness Testing
Day 9: Evaluating Responsible AI Programmes and Driving Improvement
Day 10: Integrated Project: M&E Plan and Governance Dashboard
The course suits professionals who produce or review evidence about AI systems and how they are governed, such as:
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.
Upcoming cohorts
CPD-Accredited
Official invoice & confirmation letter provided
Team discount for 3+ seats
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Everything you need to know about this course before you register.
By the end of the Monitoring and Evaluation of AI Governance, Responsible AI & ISO 42001 programme, you'll 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, and measure fairness, explainability and the effectiveness of human oversight with recognised metrics, stating their limitations. The full breakdown of topics is covered session by session in the Course Outline tab above.
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, and Digital programme officers in development agencies and foundations.
Monitoring and Evaluation of AI Governance, Responsible AI & ISO 42001 Training Course typically runs as 10 Days. It's available as in-person classroom, live virtual, and in-house corporate training — every course can also be delivered on-site for your team on dates that suit you.
Monitoring and Evaluation of AI Governance, Responsible AI & ISO 42001 Training Course is scheduled in-classroom in Nairobi, Kenya, Mombasa, Kenya, Naivasha, Kenya, and Kisumu, Kenya, and 14 other locations, plus a live interactive virtual classroom you can join from anywhere. Check the schedule panel above for exact upcoming dates and fees in each location.
The next live virtual cohort of Monitoring and Evaluation of AI Governance, Responsible AI & ISO 42001 starts October 19, 2026, with new classroom cohorts also running on a rolling basis. Pick a date and location in the schedule panel above, then click "Register for the Course" — it takes a few minutes and your seat is confirmed once payment or a signed purchase order is received.
Yes — delegates who meet the attendance requirement receive a Certificate of Completion for Monitoring and Evaluation of AI Governance, Responsible AI & ISO 42001 Training Course from Vision Reach Global Consultancy, issued in the name you register with, so double-check the spelling at checkout.
Monitoring and Evaluation of AI Governance, Responsible AI & ISO 42001 Training Course is pitched at intermediate professionals. If you're unsure whether it's the right fit for your current role or background, message our training advisors before you register and they'll help you confirm.
Fees for Monitoring and Evaluation of AI Governance, Responsible AI & ISO 42001 Training Course vary by delivery location and format and are shown in real time in the schedule panel above once you pick a date. Register 3 or more delegates on the same course together and a 5% team discount is applied automatically — larger cohorts can request a custom corporate quote.
Yes — Monitoring and Evaluation of AI Governance, Responsible AI & ISO 42001 Training Course can be delivered on-site at your offices (or virtually for distributed teams), with case studies and examples tailored to your industry and the specific challenges your team is working through. Switch to the "In-House" tab in the schedule panel above to request a proposal.
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