Postgraduate Diploma in AI Governance for AD and EU AI Act
Online 800-hour Postgraduate Diploma in AI Governance for AD and EU AI Act. 8 modules covering Regulation (EU) 2024/1689, ISO/PAS 8800:2024 and MLflow. Assigned tutor and open enrolment.
Take the controls.
Run a real engineering trade-off from this programme. Move the sliders, watch the model react in real time and beat the challenge.
What you will be able to do
Identify the AI components of an automated driving system.
Interpret the requirements of Regulation (EU) 2024/1689.
Apply ISO/PAS 8800 to the model lifecycle.
Implement an AI management system in accordance with ISO/IEC 42001.
Manage AI risks using recognised frameworks.
Plan conformity and post-market surveillance.
Integrate ethical criteria into automated driving decisions.
Who it's for
- AI governance manager looking to specialise in AI Governance for AD and EU AI Act.
- AI safety engineer looking to specialise in AI Governance for AD and EU AI Act.
- Regulatory compliance specialist looking to specialise in AI Governance for AD and EU AI Act.
- AI systems auditor looking to specialise in AI Governance for AD and EU AI Act.
- Technology risk manager looking to specialise in AI Governance for AD and EU AI Act.
- Responsible AI consultant looking to specialise in AI Governance for AD and EU AI Act.
- Graduates in engineering or science seeking to join car manufacturers.
What you'll work with
- Reglamento (UE) 2024/1689
- ISO/PAS 8800:2024
- ISO/IEC 42001:2023
- ISO/IEC 23894:2023
- NIST AI RMF 1.0
- ISO/IEC 5338:2023
- ISO 39003:2023
- Reglamento (UE) 2016/679
8 modules · 800 h
1.1AI components in the driving stack1.2Learned perception, prediction and planning1.3End-to-end models and foundation models1.4Specific risks of machine learning1.5Opacity, bias and data drift1.6Differences between classical software and trained models1.7European and international regulatory ecosystem1.8Liability of the manufacturer and the operator1.9Social expectations and public acceptance1.10Case study: map of AI components of a robotaxi
2.1Structure of Regulation (EU) 2024/16892.2Classification of high-risk systems2.3Relationship with vehicle type-approval legislation2.4Risk management requirements of Article 92.5Data governance under Article 102.6Technical documentation and records2.7Transparency and human oversight2.8Accuracy, robustness and cybersecurity2.9Application timeline and delegated acts2.10Workshop: applicability analysis of the regulation to an ADS
3.1Scope and relationship with ISO 26262 and ISO 214483.2AI safety lifecycle3.3Safety requirements for models3.4Dataset specification and quality3.5Model insufficiencies and their causes3.6Architectural mitigation measures3.7Verification and validation of models3.8Monitoring in operation3.9Safety argument for AI components3.10Workshop: safety plan for an object detector
4.1Structure of ISO/IEC 420014.2Policy, roles and responsibilities4.3AI system impact assessment4.4Risk management under ISO/IEC 238944.5NIST AI RMF 1.0 framework4.6Integration with the IATF 16949 quality system4.7Management of model and data suppliers4.8Performance indicators of the management system4.9Internal audit and management review4.10Practice: statement of applicability for AI controls
5.1ISO/IEC 5338 processes5.2Decision gates from data to deployment5.3Model documentation and data sheets5.4Change management and retraining5.5Version control of data and models5.6Robustness and fairness assessment5.7Human oversight in operation5.8Event logging and explainability5.9Model withdrawal and end of life5.10Workshop: model card for a trajectory predictor
6.1High-risk conformity assessment6.2Required quality management system6.3Marking, declaration and database registration6.4Post-market surveillance6.5Reporting of serious incidents6.6Interaction with market surveillance authorities6.7Coordination with UNECE R155 and R1566.8Penalties and liability regime6.9Assessment of impact on fundamental rights6.10Case study: post-market surveillance plan
7.1European Commission ethical principles for connected vehicles7.2Dilemmas and decisions in critical situations7.3ISO 39003 guidelines7.4Fairness towards vulnerable users7.5Privacy and personal data protection7.6Communicating capabilities and limitations7.7Stakeholder engagement7.8Ethics committees and internal governance7.9Transparency towards the public7.10Practice: ethical assessment of a driving policy
8.1Definition of the AI system8.2Regulatory applicability analysis8.3AI risk assessment8.4Design of the management system8.5Data governance plan8.6Required technical documentation8.7Surveillance and oversight plan8.8Compliance roadmap8.9Report and presentation8.10Final review with the tutor
How you'll learn
Fully 100 % online training on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. No fixed timetable: the student progresses at their own pace within the intake, which makes it possible to combine with work.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is MLflow, Weights & Biases, IBM watsonx.governance, Fairlearn, Collibra, in free, academic or evaluation versions. The practical work is based on real procedures for AI impact assessments, AI management system audits and technical documentation reviews of models. Assessment is continuous per module plus the final integrating project.
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Identify the AI components of an automated driving system.
Interpret the requirements of Regulation (EU) 2024/1689.
Apply ISO/PAS 8800 to the model lifecycle.
Implement an AI management system in accordance with ISO/IEC 42001.
Manage AI risks using recognised frameworks.
Where this takes you
Requirements
- Prior technical training or professional experience in the sector. A university degree is not required.
- Internet connection and a computer capable of running the programme's software.
- Sufficient level of Spanish to follow the reading of technical regulatory texts.
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 consists of 800 hours in total, spread over 6 to 8 months. That is an average of 10 to 15 hours per week, compatible with a full-time job.
The programme works with MLflow, Weights & Biases, IBM watsonx.governance, Fairlearn, Collibra. Free, academic or evaluation versions are used, and the tutor indicates the free alternative where one exists.
An in-house diploma from SEIUM Institute upon passing the continuous assessment and the integrating project. It is in-house training, not an official Ministry qualification.
Yes. Modules passed in a course or postgraduate diploma in the same area are credited towards the higher programme. Please consult the credit transfer policy when you enrol.
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