Monitoring and Evaluation of AI Governance, Responsible AI & ISO 42001 Training Course in Zanzibar, Tanzania
Classroom training in Zanzibar · next cohort Nov 16, 2026 · from USD 5,200
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.
At a Glance
Training in Zanzibar, Tanzania
Zanzibar is Tanzania's island archipelago, a popular setting for residential training and leadership retreats.
More about training in Zanzibar →- Format
- Classroom, in Zanzibar
- Duration
- Mon - Fri (10 Days)
- Fee
- USD 5,200
- Next start dates
- Nov 16, 2026, Dec 14, 2026, Jan 11, 2027
- Certificate
- Certificate of Completion
- Airport
- Abeid Amani Karume International (ZNZ)
- From Dar es Salaam
- Fast ferry to Stone Town, about two hours, or a short flight
- Time zone
- East Africa Time (UTC+3)
- Currency
- Tanzanian shilling (TZS). Fees are listed in USD.
About this 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.
Training Objectives
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.
What you'll learn
Key Takeaways
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.
Training Methodology
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
Day 1: Why AI Governance Must Be Measured
- From policy on paper to evidence of control: the measurement gap
- ISO/IEC 42001 clause 9: monitoring, measurement, analysis, evaluation, internal audit and management review
- The NIST AI RMF measure function and how it links to govern, map and manage
- Logging and post-market monitoring duties for high-risk systems under the EU AI Act
- Who measures what: system owners, the second line, internal audit and external assessors
- Introducing the course case portfolio: credit scoring, recruitment screening and a chatbot
- Baseline exercise: what evidence exists today for each system
Day 2: Results Frameworks and Theories of Change for Responsible AI
- Logic models, theories of change and results chains applied to AI governance
- Outputs versus outcomes: approved policies versus reduced harm
- Stating assumptions about adoption, behaviour and technology
- Building a results framework with baselines, targets and milestones
- Adapting the OECD-DAC criteria to AI governance
- Stakeholder mapping: affected people, users, regulators and funders
- Workshop: a theory of change for an institutional responsible AI programme
Day 3: Designing Indicators for an AI Management System
- KPIs, KRIs and key control indicators: definitions and differences
- Management system indicators: inventory coverage, completed impact assessments, training and overdue actions
- System-level indicators: error rates, complaints, overrides and appeals
- Setting thresholds, tolerances and escalation triggers
- Indicator reference sheets and SMART criteria
- Avoiding vanity metrics and perverse incentives
- Exercise: an indicator set for the case portfolio
Day 4: Monitoring Models in Production
- Performance metrics for classification and regression: precision, recall, AUC, calibration and error
- Data drift, concept drift and delayed ground truth
- Statistical tests: population stability index, Kolmogorov-Smirnov and Jensen-Shannon divergence
- Data quality monitoring: completeness, validity and pipeline failures
- Logging, model versioning and audit trails
- Alert design and response runbooks
- Demonstration: tracing a performance drop to its cause
Day 5: Measuring Fairness, Transparency and Human Oversight
- Fairness definitions and why they conflict: demographic parity, equalised odds and predictive parity
- Subgroup analysis and the problem of missing protected attributes
- ISO/IEC TR 24027 on bias in AI systems and AI-aided decision making
- Explainability evidence: SHAP values, feature importance and reason codes
- Measuring human oversight: override rates, review times and automation bias
- Transparency indicators: notices, documentation and model cards
- Exercise: interpreting a fairness report for the recruitment tool
Day 6: Applied Monitoring Lab: Drift and Fairness in Python
- Setting up a Python notebook environment with pandas
- Computing performance and drift metrics on a sample credit model
- Fairness assessment with Fairlearn's MetricFrame
- Generating drift reports with Evidently
- Simulating a data shift and tracking its effect over time
- Turning outputs into indicator values and alerts
- Documenting results for audit and review
Day 7: Monitoring Generative AI and Third-Party Models
- Evaluation dimensions for large language models: accuracy, groundedness, harmful content and refusal behaviour
- Building test sets and reference answers for a chatbot
- Human rating protocols and inter-rater agreement
- Red-team findings, prompt injection and data leakage tests
- Using the NIST Generative AI Profile to frame measures
- Supplier monitoring: model version changes, service levels and incident notifications
- Lab: scoring a batch of chatbot responses and reporting trends
Day 8: Internal Audit and Control Effectiveness Testing
- Audit programme planning under ISO/IEC 42001 clause 9.2 and ISO 19011
- Audit criteria, sampling and evidence for Annex A controls
- Testing the design and operating effectiveness of AI controls
- Auditing impact assessments, data controls and life cycle records
- Interviewing system owners and reviewing technical documentation
- Grading nonconformities and writing findings that drive action
- Simulation: auditing the chatbot and the recruitment tool
Day 9: Evaluating Responsible AI Programmes and Driving Improvement
- Evaluation questions, design matrices and the choice of methods
- Contribution analysis and outcome harvesting for governance programmes
- Surveys, interviews and complaints data as evidence of affected people's experience
- Maturity assessments and evaluation rubrics
- Management review inputs and outputs under clause 9.3
- Root cause analysis, corrective action and continual improvement under clause 10
- Learning loops: feeding findings back into policy and model design
Day 10: Integrated Project: M&E Plan and Governance Dashboard
- Assembling the results framework, indicators and data sources
- Building a Power BI dashboard for AI governance performance
- Reporting formats for boards, regulators and funders
- Quality assurance for the monitoring data itself
- Budgeting and staffing monitoring and evaluation activities
- Presentation of plans and peer review
- Action planning and course close
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
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.
Choose Where You Want to Learn
Monitoring and Evaluation of AI Governance, Responsible AI & ISO 42001 Training Course Venues
TanzaniaZanzibarNext Nov 16· 13 dates$5,200Viewing- TanzaniaArushaNext Nov 9· 13 dates$3,800
- TanzaniaDodomaNext Oct 26· 13 dates$3,600
- TanzaniaDar es SalaamNext Oct 26· 13 dates$4,000
KenyaNairobiNext Nov 9· 13 dates$3,000- KenyaMombasaNext Oct 26· 13 dates$3,200
- KenyaNaivashaNext Nov 2· 13 dates$3,000
- KenyaKisumuNext Oct 26· 13 dates$3,000
- KenyaMachakosNext Nov 2· 13 dates$3,200
UgandaKampalaNext Nov 2· 13 dates$3,800
RwandaKigaliNext Oct 26· 13 dates$3,800
NigeriaLagosNext Nov 16· 13 dates$5,600- NigeriaAbujaNext Nov 9· 13 dates$5,600
South AfricaPretoriaNext Nov 2· 13 dates$6,400- South AfricaJohannesburgNext Nov 2· 13 dates$6,800
- South AfricaCape TownNext Oct 26· 13 dates$7,600
UAEDubaiNext Oct 26· 13 dates$7,600
GhanaAccraNext Nov 16· 13 dates$9,000
Scroll sideways to see all 18 cities →
Frequently Asked Questions About Monitoring and Evaluation of AI Governance, Responsible AI & ISO 42001 Training Course in Zanzibar
Everything you need to know about this course before you register.
Monitoring and Evaluation of AI Governance, Responsible AI & ISO 42001 Training Course runs as classroom training in Zanzibar, Tanzania, in Monday-to-Friday cohorts that start every four weeks. The venue, daily timings and joining details are sent in your Joining Instructions email ahead of the start date.
The next Zanzibar cohort starts on November 16, 2026, and a new intake follows every four weeks. Pick a date under "Upcoming sessions in Zanzibar" above and click Register; your seat is confirmed once payment or a signed purchase order is received.
The classroom fee for Monitoring and Evaluation of AI Governance, Responsible AI & ISO 42001 Training Course in Zanzibar is USD 5,200 per delegate, covering tuition and course materials. Register 3 or more delegates together and a 5% team discount is applied automatically.
Yes. Besides Zanzibar, Monitoring and Evaluation of AI Governance, Responsible AI & ISO 42001 Training Course runs as classroom training in Arusha, Dodoma, and Dar es Salaam. It also runs as live online training you can join from anywhere in Tanzania, and in-house at your organisation's own premises.
Yes — Monitoring and Evaluation of AI Governance, Responsible AI & ISO 42001 Training Course can be delivered at your offices in Zanzibar or at a venue you choose, on dates that suit your team, with case studies tailored to your sector. Message our training advisors to request a proposal.
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.
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.
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