Governance & Compliance

Certificate in AI Governance

This course equips professionals with the knowledge and skills to implement effective AI governance frameworks. Participants will explore regulatory compliance, ethical considerations, risk management, and accountability mechanisms essential for responsible AI deployment in their organisations.

5 days Classroom or live online English

Smart Horizon Certified - Global Leadership Consultants

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Download the full standard outline: why attend, methodology, objectives, audience, competencies, the daily outline and current plans.

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Why attend

From knowledge to real-world impact.

AI technologies are rapidly evolving, creating significant challenges in governance, compliance, and ethical use. Professionals must navigate complex regulations and ensure that AI systems align with organisational values and societal norms. Failure to implement robust governance can lead to legal repercussions, reputational damage, and loss of stakeholder trust. This course addresses these pressing issues by providing participants with the tools necessary to assess compliance risks, develop ethical guidelines, and create frameworks that ensure accountability in AI deployment. Participants will learn to identify potential pitfalls and implement strategies that foster trust and transparency in AI systems.

Participants will develop practical capabilities to assess AI risks, design governance frameworks, and implement accountability measures. Through hands-on exercises, they will engage in real-world scenarios that require critical decision-making and strategic planning, ensuring they can effectively manage AI initiatives within their organisations. By the end of the course, attendees will be equipped to lead discussions on ethical AI use, facilitate stakeholder engagement, and create comprehensive governance strategies that align with both regulatory requirements and organisational objectives. This course empowers professionals to take proactive steps in shaping the future of AI governance.

Workplace challenges

Professional Problems This Course Is Designed to Solve

The programme explores practical approaches to the following challenges. Results depend on how the learning is applied in your organisation.

Difficulty translating subject knowledge into practical workplace decisions.
Inconsistent approaches to the activities covered in the course outline.
Lack of a structured plan for applying and reviewing new skills.

Course methodology

How you will learn.

The course employs a blend of case studies, interactive workshops, and group discussions to foster practical understanding of AI governance. Participants will engage in simulations that mimic real-world challenges, allowing them to apply theoretical concepts to practical situations. Specific exercises include risk assessment workshops, framework design sessions, and stakeholder negotiation role-plays. Outputs will include governance framework proposals, risk assessment reports, and compliance checklists, demonstrating their ability to integrate learned principles into their work. This hands-on approach ensures that participants not only learn the concepts but also gain confidence in applying them to their own organisational contexts, enhancing their professional capabilities.

Course objectives

What you'll take away.

By the end of this course, participants will be able to:

Analyse existing AI governance frameworks and identify gaps in compliance and ethics.
Design an AI governance framework tailored to organisational needs and regulatory requirements.
Assess AI-related risks and develop mitigation strategies for identified issues.
Compare ethical considerations in AI deployment across different sectors and jurisdictions.
Construct a stakeholder engagement plan to communicate governance strategies effectively.
Justify the importance of transparency and accountability in AI systems to stakeholders.

Target audience

Who should attend.

This course is suitable for:

AI project managers responsible for governance oversight.
Compliance officers ensuring adherence to regulations.
Data scientists and engineers involved in AI system development.
Ethics officers focused on responsible AI deployment.

Target competencies

Capabilities you will build.

Participants will develop competencies in:

Evaluate regulatory requirements for AI governance.
Implement risk management strategies for AI technologies.
Facilitate stakeholder discussions on AI ethics and governance.
Develop frameworks for monitoring AI system performance.
Create documentation for compliance and accountability in AI initiatives.
Lead training sessions on AI governance best practices.

Course outline

Your learning journey.

A focused, practical outline with time to explore, apply and reflect.

Day 1Foundations of AI Governance

Applied activity: Participants will analyse a case study of an organisation facing governance challenges with its AI systems. They will identify compliance gaps, ethical dilemmas, and stakeholder concerns presented in the scenario, proposing initial solutions and strategies for improvement.

Output: A report summarising identified gaps and initial recommendations for governance improvements.

09:00–09:54Introduction to AI governance concepts and frameworks, including key definitions and principles.
09:54–10:48Overview of the regulatory landscape affecting AI technologies across jurisdictions.
10:48–11:00Ethical implications of AI deployment in business, focusing on fairness and accountability.
11:00–11:30Coffee break
11:30–12:12Ethical implications of AI deployment in business, focusing on fairness and accountability. (continued)
12:12–13:06Key stakeholders in AI governance and their roles in shaping policies and practices.
13:06–14:00Understanding the importance of transparency and accountability in AI systems and their impact on trust.

Day 2Risk Assessment in AI Systems

Applied activity: Working in groups, participants will assess a fictional AI project for potential risks, using a structured risk assessment tool to evaluate impact and likelihood. They will present their findings and proposed mitigation strategies to the class, fostering collaborative learning and discussion.

Output: A completed risk assessment matrix with proposed mitigation strategies and a presentation summarising their analysis.

09:00–09:54Identifying risks associated with AI technologies, including bias, data privacy, and operational risks.
09:54–10:48Methods for assessing AI-related risks in projects, utilising risk assessment frameworks and tools.
10:48–11:00Developing risk mitigation strategies for AI initiatives, focusing on practical implementation.
11:00–11:30Coffee break
11:30–12:12Developing risk mitigation strategies for AI initiatives, focusing on practical implementation. (continued)
12:12–13:06Tools for monitoring AI system performance and compliance, including metrics and reporting mechanisms.
13:06–14:00Case studies of AI failures due to poor governance, analysing lessons learned and best practices.

Day 3Designing AI Governance Frameworks

Applied activity: Participants will design a governance framework for a fictional organisation, considering its specific AI applications and regulatory environment. They will present their framework to the class, highlighting key components, rationale, and expected outcomes from implementation.

Output: A governance framework document outlining policies, procedures, and accountability measures, along with a presentation.

09:00–09:54Components of an effective AI governance framework, including policies, procedures, and oversight mechanisms.
09:54–10:48Aligning governance frameworks with organisational objectives and regulatory requirements for compliance.
10:48–11:00Strategies for stakeholder engagement in governance design, ensuring diverse perspectives are included.
11:00–11:30Coffee break
11:30–12:12Strategies for stakeholder engagement in governance design, ensuring diverse perspectives are included. (continued)
12:12–13:06Best practices for documenting governance processes, including templates and guidelines for consistency.
13:06–14:00Evaluating the effectiveness of governance frameworks through metrics and continuous improvement.

Day 4Ethics and Accountability in AI

Applied activity: Participants will role-play as stakeholders in a fictional organisation to discuss ethical dilemmas presented by an AI system. They will negotiate solutions and accountability measures, considering diverse perspectives and potential impacts on the organisation and society.

Output: A summary of the negotiated ethical guidelines and accountability measures agreed upon, along with a reflection on stakeholder engagement.

09:00–09:54Exploring ethical considerations in AI development and deployment, including fairness, transparency, and accountability.
09:54–10:48Accountability mechanisms for AI decision-making processes, ensuring responsible use and oversight.
10:48–11:00The role of bias and fairness in AI systems, discussing strategies for mitigation and monitoring.
11:00–11:30Coffee break
11:30–12:12The role of bias and fairness in AI systems, discussing strategies for mitigation and monitoring. (continued)
12:12–13:06Strategies for fostering an ethical AI culture within organisations, including training and awareness initiatives.
13:06–14:00Engaging stakeholders in ethical discussions around AI, facilitating dialogue and consensus-building.

Day 5Integrating AI Governance into Business Strategy

Applied activity: Participants will integrate their governance frameworks into a comprehensive business strategy for a fictional organisation, considering implementation challenges, stakeholder buy-in, and long-term sustainability. They will present their strategic plans to the class for feedback and discussion.

Output: A strategic plan document that includes governance integration, training, and evaluation methods, along with a presentation to peers.

09:00–09:54Aligning AI governance with overall business strategy, ensuring coherence and support from leadership.
09:54–10:48Developing training and communication plans for governance implementation, fostering organisational buy-in.
10:48–11:00Monitoring and evaluating AI governance effectiveness over time, using feedback and performance metrics.
11:00–11:30Coffee break
11:30–12:12Monitoring and evaluating AI governance effectiveness over time, using feedback and performance metrics. (continued)
12:12–13:06Creating a culture of continuous improvement in AI governance, encouraging innovation and adaptation.
13:06–14:00Final project presentations and peer feedback, showcasing the integration of governance frameworks into business strategies.

The timings provided are estimates and may vary depending on the number of participants and the discussions taking place during the sessions. They are indicative only and do not constitute a binding commitment.

Dates & venues

Find a session that works for you.

Confirmed dates accept registration requests. Tentative dates are enquiry only.

Good to know

A few things to know.

How is the course delivered?

Our classroom programmes combine facilitated discussion, case studies, exercises and reflection. The precise format will be confirmed before enrolment.

Can you deliver this programme for our team?

Yes, you can enquire about a tailored programme. Share your goals, group size and preferred dates with our learning team.

Will I receive a certificate?

Participants who complete the programme receive a Smart Horizon certificate of attendance. Smart Horizon programmes are not externally accredited and do not award a professional qualification, licence or designation. Where a course title or outline refers to a professional certification (for example, AACE International's Certified Cost Professional), the programme supports learning and preparation only; the credential itself is awarded solely by the relevant professional body under its own application and examination requirements.

What should I confirm before booking travel?

Please confirm the session, exact venue, accessibility arrangements, fees and any visa requirements with the team before arranging travel. Tentative dates are not confirmed sessions.

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