Course in Ethics in Ethical AI Governance
Online 300 h course in ethics in ethical AI governance. 6 modules covering ISO/IEC 42001 (AI management systems), Regulation (EU) 2024/1689 (Artificial Intelligence Act) and Python (pandas). Assigned tutor and open enrolment.
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What you will be able to do
Interpret international principles of trustworthy AI.
Implement an AI management system compliant with ISO/IEC 42001.
Apply the obligations of the AI Act according to risk level.
Design AI governance bodies and processes.
Assess AI risks throughout their lifecycle.
Plan audit and assurance activities.
Develop an ethical AI governance framework for an organisation.
Who it's for
- AI governance manager wishing to specialise in Ethics in Ethical AI Governance.
- AI systems auditor wishing to specialise in Ethics in Ethical AI Governance.
- Consultant in AI Act compliance wishing to specialise in Ethics in Ethical AI Governance.
- Technology risk manager wishing to specialise in Ethics in Ethical AI Governance.
- AI ethics specialist wishing to specialise in Ethics in Ethical AI Governance.
- AI assurance manager wishing to specialise in Ethics in Ethical AI Governance.
- Engineering or science graduates seeking to enter technology consulting.
What you'll work with
- ISO/IEC 42001 (AI management systems)
- Regulation (EU) 2024/1689 (Artificial Intelligence Act)
- NIST AI RMF 1.0 (AI risk management framework)
- ISO/IEC 23894 (AI risk management)
- ISO/IEC 38507 (governance of IT and AI)
- UNESCO Recommendation on the Ethics of AI (2021)
- IEEE 7000 (system design with ethical considerations)
6 modules · 300 h
1.1Ethics guidelines of the EU expert group1.2OECD Principles on AI1.3UNESCO Recommendation on the Ethics of AI1.4Council of Europe Framework Convention on AI1.5From principle to practice: the implementation gap1.6Deontological, consequentialist and virtue ethics1.7Conflicting values and their weighting1.8Machine ethics versus organisational ethics1.9Overview of governance initiatives1.10Case study: comparison of three principle frameworks
2.1ISO/IEC 42001: management system requirements2.2ISO/IEC 23894 on AI risk management2.3NIST AI Risk Management Framework2.4IEEE 7000 value-based design2.5Policies, roles and responsibilities2.6AI systems inventory2.7AI system impact assessment2.8Annex A controls of ISO/IEC 420012.9Integration with ISO/IEC 27001 and GDPR2.10Practice: statement of applicability for ISO/IEC 42001
3.1Regulation (EU) 2024/1689: structure and risk-based approach3.2Obligations of providers and deployers3.3General-purpose AI models and systemic risk3.4Harmonised standards and presumption of conformity3.5Spanish Agency for the Supervision of AI3.6Regulatory sandbox in Spain3.7Regulation in the United States, China and the United Kingdom3.8Civil liability for AI damage3.9The liability exemption and its debate3.10Case study: obligations map for a high-risk provider
4.1AI ethics committees: composition and mandate4.2Chief AI Officer and compliance functions4.3Ethical review across the lifecycle4.4Documentation: model cards and datasheets4.5Internal and external AI auditing4.6AI incident management4.7Incident databases: AIID4.8AI training and literacy (Article 4)4.9Organisational culture and internal whistleblowing4.10Workshop: designing an AI ethics committee
5.1Metrics for robustness, fairness and explainability5.2Red teaming of language models5.3Evaluation of foundation models5.4Real-world testing and monitoring5.5Certification and CE marking5.6Governance of training data5.7Intellectual property and data5.8Sustainability and energy consumption of AI5.9Governance maturity indicators5.10Case study: audit of a deployed language model
6.1Selection of the organisation and its context6.2Inventory and classification of systems6.3Gap analysis against ISO/IEC 420016.4Design of policies and roles6.5Impact assessment procedure6.6Regulatory compliance plan6.7Indicators and dashboard6.8Implementation roadmap6.9Report and presentation6.10Final review with the tutor
How you'll learn
100 % online training on the SEIUM campus, with downloadable material, recorded classes and a assigned technical tutor. No fixed timetables: students progress at their own pace within the intake, which makes it possible to combine the programme with work.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is Python (pandas), Jira, advanced Excel, Power BI, in free, academic or evaluation versions. Tests are studied using real procedures for audits of AI management systems, design of AI ethics committees and conformity assessments. Assessment is continuous, module by module, plus the final integrative project.
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Interpret international principles of trustworthy AI.
Implement an AI management system compliant with ISO/IEC 42001.
Apply the obligations of the AI Act according to risk level.
Design AI governance bodies and processes.
Assess AI risks throughout their lifecycle.
Where this takes you
Requirements
- No prior qualification is required.
- Internet connection and a computer capable of running the programme's software.
- Sufficient Spanish level to follow technical regulatory reading.
Ready to start?
Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.
Frequently asked questions
The programme totals 300 hours spread over 3 to 5 months. That is an average of 10 to 15 hours per week, compatible with full-time work.
The programme uses Python (pandas), Jira, advanced Excel and Power BI. Free, academic or evaluation versions are used, and the tutor indicates the open-source alternative where one exists.
A SEIUM own diploma upon passing continuous assessment and the integrative project. It is in-house training, not an official qualification from the Ministry.
Yes. Modules passed in a Course or Postgraduate Diploma in the same area are recognised towards the higher-level programme. Check the recognition policy when you enrol.
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