Course in International Standards for Responsible AI
Online 300-hour course in international standards for responsible AI. 6 modules covering ISO/IEC 42001:2023 (AI management system), Regulation (EU) 2024/1689 (AI Act) and Python (Fairlearn). 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
Define the principles of responsible artificial intelligence.
Apply ISO/IEC 23894 to the risk management of an AI system.
Assess data and models against bias and fairness issues.
Apply transparency and explainability techniques.
Classify systems according to the risk levels of the European AI Act.
Design an AI management system compliant with ISO/IEC 42001.
Develop a responsible governance plan for the lifecycle of a system.
Who it's for
- AI governance manager looking to specialise in International Standards for Responsible AI.
- AI systems auditor looking to specialise in International Standards for Responsible AI.
- Responsible machine learning engineer looking to specialise in International Standards for Responsible AI.
- AI regulatory compliance consultant looking to specialise in International Standards for Responsible AI.
- Technology risk manager looking to specialise in International Standards for Responsible AI.
- Data ethics analyst looking to specialise in International Standards for Responsible AI.
- Engineering or science graduates looking to enter technology companies.
What you'll work with
- ISO/IEC 42001:2023 (AI management system)
- Regulation (EU) 2024/1689 (AI Act)
- ISO/IEC 23894:2023 (AI risk management)
- NIST AI RMF 1.0
- ISO/IEC TR 24027 (bias in AI systems)
- ISO/IEC 22989 (AI concepts and terminology)
- IEEE 7000-2021 (design with ethical considerations)
- UNESCO Recommendation on the Ethics of Artificial Intelligence (2021)
6 modules · 300 h
1.1Dimensions: fairness, robustness, transparency and accountability1.2From principle to verifiable requirement1.3Overview of frameworks and standards1.4Difference between principle, technical standard and law1.5Standardisation bodies involved1.6Artificial intelligence management standards1.7Risk management standards for learning systems1.8National reference frameworks1.9Field vocabulary and acronyms1.10Exercise: translating a principle into requirements
2.1Identifying the context of use and affected parties2.2Taxonomy of machine learning risks2.3System impact assessment2.4Risk level and proportionate measures2.5Security risks and adversarial attacks2.6Robustness to distribution shifts2.7Model uncertainty and its communication2.8Risk treatment plan2.9Residual risk and its acceptance2.10Practice: risk assessment of a system
3.1Dataset documentation3.2Provenance, licences and consent3.3Representativeness and population coverage3.4Types of bias and their origin3.5Fairness metrics and their mutual incompatibility3.6Mitigation before, during and after training3.7Data protection and minimisation3.8Synthetic data and its limits3.9Data governance and traceability3.10Practice: auditing a model for bias
4.1Model documentation and technical datasheet4.2Local and global explainability4.3Limits of explanation techniques4.4Design of the evaluation set4.5Performance metrics and their selection4.6Subgroup testing and error analysis4.7Adversarial and robustness testing4.8Human oversight and its effective design4.9Decision logging and auditability4.10Practice: drafting the technical datasheet of a model
5.1Artificial intelligence management system5.2Roles, responsibilities and review committee5.3European artificial intelligence regulation and obligations5.4System classification and requirements by category5.5Conformity assessment and marking5.6Post-deployment monitoring5.7Drift detection and retraining5.8Incident management and notification5.9Internal audit and certification5.10Practice: designing an organisation's management system
6.1Choice of system and its context of use6.2Impact and risk assessment6.3Data documentation and bias analysis6.4Evaluation and testing plan6.5Design of human oversight6.6Technical datasheet and transparency documentation6.7Monitoring and incident management plan6.8Regulatory compliance matrix6.9Project report6.10Defence of the work
How you'll learn
Training 100 % online on the SEIUM campus, with downloadable material, recorded classes and assigned technical tutor. No fixed timetables: the learner progresses at their own pace within the intake, which allows it to be combined with work.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is Python (Fairlearn), IBM AI Fairness 360, SHAP, MLflow, in free, academic or evaluation versions. Case studies are based on real procedures for model bias evaluation, compliance auditing against the AI Act and explainability analysis. Assessment is continuous per 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.
Define the principles of responsible artificial intelligence.
Apply ISO/IEC 23894 to the risk management of an AI system.
Assess data and models against bias and fairness issues.
Apply transparency and explainability techniques.
Classify systems according to the risk levels of the European AI Act.
Where this takes you
Requirements
- No prior degree required.
- Internet connection and a computer capable of running the programme's software.
- Sufficient level of Spanish 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 between 10 and 15 hours per week on average, compatible with a full-time job.
You work with Python (Fairlearn), IBM AI Fairness 360, SHAP and MLflow. Free, academic or evaluation versions are used, and the tutor indicates the free alternative where one exists.
An own diploma from SEIUM on passing the continuous assessment and the integrative project. It is in-house training, not an official Ministry title.
Yes. Modules passed in a course or postgraduate diploma in the same area are recognised in the advanced programme. Check the recognition policy when enrolling.
Related programmes
Railway Signaling Course: Basic Principles
The Railway Signaling: Basic Principles course introduces the essential fundamentals for understanding and applying railway signaling systems. It explores the principles of railway safety, signals and…
Rail-Airport Link and Connectivity Course
The Rail-Airport Link and Connectivity course explores the development and management of transport projects integrating trains and airports, covering infrastructure planning, station design, and passenger demand…
Course in Energy Transition Strategies in Cities
Online 300 h course in Energy Transition Strategies in Cities. 6 modules covering Directive (EU) 2018/2001 on the promotion of the use of energy from renewable…
More in Restoration & Classics
All Restoration & Classics programmes → · How to apply · Fees & financing · Scholarships