Course on Dataset Auditing for the AI Act in Mobility
Online 300-hour course in dataset auditing for the AI Act in mobility. 6 modules covering Regulation (EU) 2024/1689 (Artificial Intelligence Act), ISO/IEC 5259 (data quality for analytics and machine learning) and Python (pandas). Assigned tutor and enrolment open.
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
Interpret the requirements of the AI Act applicable to high-risk AI systems in mobility.
Define data quality, representativeness and traceability criteria in accordance with ISO/IEC 5259.
Analyse perception and driving datasets to detect bias and coverage gaps.
Apply statistical techniques and audit tools to labelled datasets.
Relate dataset quality to functional safety and SOTIF.
Document the data governance required in the technical documentation of the system.
Carry out the full audit of an automated driving dataset.
Who it's for
- AI systems auditor who wants to specialise in Dataset Auditing for the AI Act in Mobility.
- Autonomous driving data engineer who wants to specialise in Dataset Auditing for the AI Act in Mobility.
- AI Act compliance specialist who wants to specialise in Dataset Auditing for the AI Act in Mobility.
- Data quality engineer who wants to specialise in Dataset Auditing for the AI Act in Mobility.
- Perception engineer who wants to specialise in Dataset Auditing for the AI Act in Mobility.
- AI governance consultant who wants to specialise in Dataset Auditing for the AI Act in Mobility.
- Engineering or science graduates looking to enter the automotive industry.
What you'll work with
- Regulation (EU) 2024/1689 (Artificial Intelligence Act)
- ISO/IEC 5259 (data quality for analytics and machine learning)
- ISO/PAS 8800 (safety of AI in road vehicles)
- ISO 21448 (SOTIF)
- ISO/IEC 42001 (AI management systems)
- ISO/IEC 23894 (AI risk management)
- Regulation (EU) 2016/679 (GDPR)
6 modules · 300 h
1.1Structure of Regulation (EU) 2024/16891.2High-risk systems and Annex I1.3Relationship with Regulation (EU) 2018/858 and 2019/21441.4Obligations of providers and deployers1.5Application timeline1.6Article 10: data and data governance1.7Harmonised standards under development1.8Interaction with the GDPR1.9Market surveillance in automotive1.10Practical case: classification of a perception system
2.1Relevance and representativeness of data2.2Errors, completeness and accuracy2.3Bias and its detection2.4Statistical properties of the set2.5Geographical, behavioural and functional context2.6Training, validation and test data2.7ISO/IEC 5259 on data quality for AI2.8ISO/PAS 8800 on AI in vehicles2.9Data lineage and provenance2.10Workshop: Article 10 requirements matrix
3.1Public datasets: KITTI, nuScenes and Waymo Open3.2Fleet data capture3.3Box, segmentation and LiDAR annotation3.4Labelling quality and inter-annotator agreement3.5Synthetic data and simulation3.6Operational design domain (ODD) coverage3.7Rare scenarios and edge cases3.8Data on vulnerable users3.9Anonymisation of faces and licence plates3.10Practice: coverage analysis of a dataset
4.1Data audit planning4.2Sampling and label review4.3Bias and fairness metrics4.4Distribution and drift analysis4.5Tools: Great Expectations and Cleanlab4.6Auditing data pipelines4.7Evidence for technical documentation4.8Datasheets for datasets4.9Management of non-conformities4.10Laboratory: detection of labelling errors
5.1Technical documentation under Annex IV5.2Quality management system under Article 175.3Record-keeping and traceability5.4Risk management under ISO/IEC 238945.5ISO/IEC 42001 and its relationship with IATF 169495.6Post-market monitoring5.7Internal and third-party conformity assessment5.8Relationship with UNECE type approval5.9Liability and claims5.10Practical case: data file for an AEB system
6.1Selection of the system and dataset6.2Scope and audit criteria6.3Representativeness analysis6.4Labelling quality review6.5Bias assessment6.6Findings and non-conformities6.7Corrective action plan6.8Documentation of evidence6.9Report and presentation6.10Final review with the tutor
How you'll learn
Training 100 % online on the SEIUM campus, with downloadable material, recorded classes and assigned technical tutor. No fixed timetable: 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), CVAT, FiftyOne, Great Expectations, Jupyter, in free, academic or evaluation versions. The tests are based on real procedures for auditing perception datasets, bias analysis and scenario coverage, and review of technical conformity documentation. 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 the requirements of the AI Act applicable to high-risk AI systems in mobility.
Define data quality, representativeness and traceability criteria in accordance with ISO/IEC 5259.
Analyse perception and driving datasets to detect bias and coverage gaps.
Apply statistical techniques and audit tools to labelled datasets.
Relate dataset quality to functional safety and SOTIF.
Where this takes you
Requirements
- No prior qualification is required.
- Internet connection and a computer capable of running the programme's software.
- Sufficient 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 an average of 10 to 15 hours per week, compatible with full-time work.
You work with Python (pandas), CVAT, FiftyOne, Great Expectations, Jupyter. Free, academic or evaluation versions are used, and the tutor indicates the free alternative where one exists.
SEIUM's own diploma upon passing the 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 credited towards the higher-level programme. Check the credit transfer policy when you enrol.
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