Course in Transparency and Ethics in AI Applied to Vehicles
Online course of 300 hours in transparency and ethics in AI applied to vehicles. 6 modules covering Regulation (EU) 2024/1689 (Artificial Intelligence Act), ISO/PAS 8800 (safety and AI in road vehicles) and Python (SHAP). Assigned tutor and open enrolment.
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Run a real engineering trade-off from this field. Move the sliders, watch the model react in real time and beat the challenge.
What you will be able to do
Identify the uses of artificial intelligence in vehicle functions.
Apply ethical principles of fairness, accountability and human oversight.
Use explainability techniques to interpret machine learning models.
Interpret the obligations of the EU AI Act for high-risk systems.
Design an AI governance framework in accordance with ISO/IEC 42001.
Assess the ethical and technical risks of on-board models.
Document a transparent AI system for drivers and authorities.
Who it's for
- AI ethics specialist looking to specialise in Transparency and Ethics in AI Applied to Vehicles.
- Trustworthy AI engineer looking to specialise in Transparency and Ethics in AI Applied to Vehicles.
- AI compliance manager looking to specialise in Transparency and Ethics in AI Applied to Vehicles.
- Algorithm auditor looking to specialise in Transparency and Ethics in AI Applied to Vehicles.
- AI governance consultant looking to specialise in Transparency and Ethics in AI Applied to Vehicles.
- Automotive data engineer looking to specialise in Transparency and Ethics in AI Applied to Vehicles.
- Engineering or science graduates seeking to enter the automotive industry.
What you'll work with
- Regulation (EU) 2024/1689 (Artificial Intelligence Act)
- ISO/PAS 8800 (safety and AI in road vehicles)
- ISO/IEC 42001 (AI management system)
- ISO/IEC 23894 (AI risk management)
- IEEE 7001 (transparency of autonomous systems)
- ISO/IEC TR 24028 (trustworthiness in AI)
- Regulation (EU) 2016/679 (GDPR)
6 modules · 300 h
1.1AI functions in ADAS and automated driving1.2Perception with neural networks1.3Trajectory prediction1.4Planning and decision-making1.5Driver monitoring systems1.6Voice assistants and personalisation1.7Fleet training data1.8SAE J3016 levels1.9Specific risks of on-board AI1.10Case study: inventory of AI in a current vehicle
2.1EU guidelines for trustworthy AI2.2German Ethics Commission report on automated driving2.3EC expert report on connected vehicles2.4Trolley dilemmas: limits of the approach2.5Fairness in pedestrian detection2.6Driver autonomy and paternalism2.7In-cabin privacy2.8Moral responsibility and design2.9Ethics of gradual deployment2.10Workshop: ethical analysis of a collision case
3.1Explainability for engineers and for users3.2Post hoc methods: SHAP, LIME and saliency maps3.3Interpretable models by design3.4Event data recorders (EDR and DSSAD)3.5Communicating system status to the driver3.6Explanation after incidents3.7Documentation of models and datasets3.8Limits of explainability in perception3.9Model version traceability3.10Laboratory: saliency maps in pedestrian detection
4.1Regulation (EU) 2024/1689 on AI4.2High-risk AI and its relationship with type approval4.3Regulation (EU) 2022/1426 on ADS4.4ISO/PAS 8800 on AI in vehicles4.5ISO 21448 SOTIF4.6UL 4600 for autonomous systems4.7GDPR and vehicle data4.8ISO/IEC 42001 as a management system4.9Market surveillance and recalls4.10Practice: matrix of applicable requirements
5.1Ethics committees in manufacturers5.2Algorithmic impact assessment5.3Bias and robustness testing5.4Validation with scenarios5.5In-service monitoring5.6Incident management and learning5.7Public communication and trust5.8Participation of vulnerable users5.9Independent audits5.10Case study: governance of an automated pilot
6.1Selection of the AI system6.2Functional and data description6.3Ethical risk analysis6.4Explainability proposal6.5Regulatory map6.6Bias testing plan6.7Governance plan6.8Recommendations to the manufacturer6.9Report and presentation6.10Final review with the tutor
How you'll learn
100 % online training on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. No fixed timetables: students progress at their own pace within the intake, making it possible to combine study with work.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is Python (SHAP), LIME, PyTorch, MLflow, Jupyter, in free, academic or evaluation versions. Trials are studied using real procedures from algorithmic impact assessments, model explainability audits and data governance reviews. Assessment is continuous, module by 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 uses of artificial intelligence in vehicle functions.
Apply ethical principles of fairness, accountability and human oversight.
Use explainability techniques to interpret machine learning models.
Interpret the obligations of the EU AI Act for high-risk systems.
Design an AI governance framework in accordance with ISO/IEC 42001.
Where this takes you
Requirements
- No prior degree is required.
- Internet connection and a computer capable of running the programme's software.
- Sufficient command of Spanish to follow 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 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.
Work is carried out with Python (SHAP), LIME, PyTorch, MLflow, Jupyter. Free, academic or evaluation versions are used, and the tutor indicates the open-source alternative where one exists.
SEIUM Institute's own diploma upon passing the continuous assessment and the integrating project. This 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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