IEEE 7000 Ethical AI System Design Training Course

Course overview

AI teams are increasingly asked to demonstrate that systems are not only accurate and secure, but also aligned with human values, stakeholder expectations, and organisational commitments. Yet ethical concerns are often handled as late-stage review comments, broad principles, or isolated risk-register entries. IEEE 7000 provides a disciplined route from ethical concerns such as bias, autonomy, privacy, transparency and wellbeing to testable system requirements. This course helps practitioners make those decisions visible, defensible and traceable throughout the AI system lifecycle.

Participants learn to apply the IEEE 7000-2021 Model Process for Addressing Ethical Concerns during System Design. They identify affected stakeholders, elicit and prioritise ethical values, translate values into ethically aligned requirements, identify value tensions, and define verification evidence. The course connects IEEE 7000 work with AI governance, risk management, data practices, assurance cases and system engineering artefacts. Participants practise writing requirements that can be assigned, tested and reviewed rather than relying on non-actionable statements such as “the system should be fair”.

Delivery combines instructor-led explanation, worked AI case studies, facilitated design workshops and structured peer review. Throughout the week, participants apply the method to a selected AI-enabled system, such as an automated decisioning, recruitment, customer-service or predictive analytics solution. They leave with an IEEE 7000 ethical design pack containing a stakeholder map, values elicitation record, value-tension analysis, ethical requirements set, traceability matrix and initial verification plan that can be adapted for active projects.

The course is suited to experienced professionals involved in AI product, data, software, assurance, risk or governance decisions who need a practical method for embedding ethical considerations into design control and delivery.

Course objectives

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

• Apply the IEEE 7000-2021 ethical value-based system design process to an AI system scenario
• Map direct, indirect and affected stakeholders using a structured stakeholder analysis
• Elicit and prioritise stakeholder values through value elicitation workshops and evidence review
• Translate ethical values into clear, testable and allocatable system requirements
• Analyse value tensions using trade-off records and ethically aligned design alternatives
• Build a requirements traceability matrix linking concerns, values, requirements, controls and verification evidence
• Define verification criteria and assurance evidence for fairness, privacy, transparency and human oversight requirements
• Produce an IEEE 7000 ethical design pack for use in project governance and design reviews

Benefits of attending

Target competencies

Who should attend

• AI Product Managers — who must turn responsible AI commitments into delivery decisions and product requirements
• Systems Engineers — who need to integrate ethical concerns into requirements engineering and architecture work
• Data Science and Machine Learning Leads — who need to surface model impacts beyond performance metrics
• AI Governance and Responsible AI Managers — who need an operational method for governing development teams
• Risk, Compliance and Internal Audit Professionals — who need traceable evidence that ethical risks have been addressed
• UX Researchers and Service Designers — who need to represent affected users and communities in AI design decisions

Requirements and prerequisites

Participants should have practical experience of at least one digital, data or AI-enabled product, service or system lifecycle. Familiarity with requirements documents, user stories, risk registers, data protection impact assessments, model documentation or design reviews is helpful, as is a working understanding of machine learning concepts such as training data, model outputs, classification and human-in-the-loop decisions. Participants should be comfortable discussing system trade-offs with technical and non-technical stakeholders. No coding, mathematical optimisation, formal ethics qualification, legal qualification or prior knowledge of IEEE standards is required.

Training methodology

The five days combine focused instructor-led sessions on IEEE 7000 clauses and artefacts with guided application to realistic AI system cases. Participants work in small design teams to map stakeholders, conduct value elicitation, write and inspect requirements, and resolve competing values through documented trade-offs. The instructor demonstrates templates, review questions and traceability techniques before teams apply them. Case discussions examine automated decisioning, model opacity, data use and human oversight. The final day includes a facilitated design review and individual application planning for a live or anticipated workplace project.

Course outline

Day 1: IEEE 7000 foundations and ethical problem framing

• IEEE 7000-2021 purpose, scope and Model Process structure
• Ethical concerns in AI-enabled system design
• Distinguishing ethical concerns, legal obligations, risks and quality attributes
• System-of-interest boundaries and lifecycle context
• Affected stakeholder categories and impact pathways
• Ethically aligned design principles and organisational policy alignment
• Ethical design artefacts, decision records and governance gates

Workshop: Participants scope an AI system of interest and produce an initial ethical concern statement, system boundary diagram and stakeholder inventory.

Day 2: Stakeholder analysis and value elicitation

• Stakeholder mapping for users, non-users, operators and affected communities
• Power, vulnerability and representation in stakeholder analysis
• Value elicitation interview and workshop techniques
• Evidence sources for stakeholder values and likely impacts
• Value categories including autonomy, justice, privacy, wellbeing and trust
• Documenting value propositions and assumptions
• Prioritising values by impact, legitimacy and design relevance

Workshop: Teams conduct a facilitated value elicitation workshop from case personas and produce a prioritised stakeholder values register.

Day 3: From values to ethical system requirements

• IEEE 7000 value-based requirements derivation
• Converting abstract values into observable system behaviours
• Functional, non-functional and constraint requirements for ethical concerns
• Requirement syntax, acceptance criteria and allocation
• Bias and fairness requirement patterns for AI systems
• Privacy, explainability and human-oversight requirement patterns
• Requirements quality inspection for ambiguity, feasibility and testability

Workshop: Participants transform selected values into a reviewed set of ethical requirements with acceptance criteria and system allocations.

Day 4: Value tensions, risk controls and assurance evidence

• Identifying conflicts between stakeholder values and system objectives
• Trade-off analysis and ethically aligned design alternatives
• Decision logs and rationale capture for value tensions
• Linking IEEE 7000 artefacts to ISO/IEC 23894 AI risk management
• Control selection for data, model, interface and operational risks
• Verification methods including testing, inspection, simulation and user evaluation
• Assurance cases and evidence claims for ethical design

Workshop: Teams resolve a documented value tension and produce a trade-off record, control set and draft assurance claim.

Day 5: Traceability, governance and workplace implementation

• End-to-end traceability from concern to verification evidence
• Ethical requirements traceability matrix construction
• Integrating ethical requirements into Agile backlogs and systems engineering baselines
• Design reviews, change control and exception management
• Alignment with ISO/IEC 42001 AI management system controls
• Supplier, model and third-party component assurance questions
• Implementation roadmap, ownership model and success measures

Workshop: Participants complete and present an IEEE 7000 ethical design pack and create a 90-day implementation plan for their organisation.

Tools & standards covered

IEEE 7000-2021, ISO/IEC 42001, ISO/IEC 23894, Jama Connect

A typical training day

Live online deliveries follow the same structure in the East Africa Time zone, with shorter screen blocks and longer breaks.

What the fee includes

How you can take this course

Certification

Participants who complete the full five days receive the Skillset Development Certificate of Completion, stating the course title, course code, dates and delivery format — suitable for professional-development records and employer reimbursement.

Frequently asked questions