Course in Algorithmic Transparency Regulation
Online 300-hour course in algorithmic transparency regulation. 6 modules covering Regulation (EU) 2024/1689 (Artificial Intelligence Act), Regulation (EU) 2016/679 (GDPR), Article 22 and Python (SHAP and LIME). Assigned tutor and enrolment open.
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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
Understand what algorithmic transparency requires and why it is necessary.
Interpret the transparency obligations of the Artificial Intelligence Act, the GDPR and the Digital Services Act.
Document algorithmic systems using model cards and records.
Apply explainability techniques such as SHAP and LIME.
Carry out algorithmic audits of bias and performance.
Design governance and publication mechanisms for algorithms.
Prepare a transparency report for an algorithmic system.
Who it's for
- Algorithm auditor wishing to specialise in Algorithmic Transparency Regulation.
- AI governance manager wishing to specialise in Algorithmic Transparency Regulation.
- Data protection officer wishing to specialise in Algorithmic Transparency Regulation.
- Model explainability analyst wishing to specialise in Algorithmic Transparency Regulation.
- Digital compliance consultant wishing to specialise in Algorithmic Transparency Regulation.
- Transparency officer in the public sector wishing to specialise in Algorithmic Transparency Regulation.
- Graduates in engineering or science seeking to enter public administration.
What you'll work with
- Regulation (EU) 2024/1689 (Artificial Intelligence Act)
- Regulation (EU) 2016/679 (GDPR), Article 22
- Regulation (EU) 2022/2065 (Digital Services Act)
- ISO/IEC 42001 (AI management systems)
- ISO/IEC 23894 (AI risk management)
- ISO/IEC TR 24028 (trustworthiness in AI)
- Law 19/2013 on transparency, access to public information and good governance
6 modules · 300 h
1.1Technical, legal and organisational opacity1.2What the person affected by a decision wants to know1.3What the supervisor needs to know1.4Transparency versus trade secrets1.5Transparency versus the risk of system manipulation1.6Levels of transparency: system, model and decision1.7International reference documents1.8Sector vocabulary1.9Public cases that drove regulation1.10Exercise: defining what to publish about a specific system
2.1Automated decisions and data subject rights2.2Right to meaningful information about the logic involved2.3European AI regulation and transparency2.4Obligations of online platforms2.5Transparency in the public sector and algorithm registers2.6Public procurement and transparency clauses2.7Access to public information applied to algorithms2.8Sector regulation: credit, employment and healthcare2.9Relevant case law2.10Practical: mapping the obligations of a public-sector system
3.1Model and dataset technical sheet3.2Decision log and audit data3.3Local explanations and common methods3.4Global explanations and feature importance analysis3.5Counterfactual explanations3.6Limits and risks of explanations3.7Explanations understandable to the affected person3.8Explanation interface and user testing3.9Verifying the fidelity of the explanation3.10Practical: drafting an explanation for an affected person
4.1Internal and external system audit4.2Auditor access to data and models4.3Black-box auditing and its techniques4.4Subgroup testing and bias detection4.5Reproducibility of the audited result4.6Protection of information during an audit4.7Audit report and publication4.8Inspection by the authority and its powers4.9Independent research and researcher access4.10Practical: designing an audit plan
5.1Public algorithm register: content and maintenance5.2Impact assessment and its publication5.3Participation of affected persons in design5.4Complaints channel and human review5.5Communication with the media and the public5.6Proactive versus reactive transparency5.7Transparency indicators5.8The risk of merely formal transparency5.9Continuous improvement of policy5.10Practical: designing the public information sheet for a system
6.1Choice of system and its context6.2Map of transparency obligations6.3Technical documentation and model sheet6.4Designing explanations for each audience6.5Audit and access plan6.6Public information sheet and algorithm register6.7Complaints channel and human review6.8Indicators and improvement plan6.9Project report6.10Project defence
How you'll learn
100% online training on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. There are no fixed timetables: students progress at their own pace within the intake, which makes it possible to combine the course with work.
Each module combines theory, guided reading of the original regulation and applied exercises. The working software is Python (SHAP and LIME), IBM AI Fairness 360, Fairlearn, Jupyter Notebook, in free, academic or evaluation versions. Practical cases are based on real procedures for algorithmic audits of real systems, preparation of model sheets and records, and explainability analysis of automated decisions. 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.
Understand what algorithmic transparency requires and why it is necessary.
Interpret the transparency obligations of the Artificial Intelligence Act, the GDPR and the Digital Services Act.
Document algorithmic systems using model sheets and records.
Apply explainability techniques such as SHAP and LIME.
Carry out algorithmic audits of bias and performance.
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 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 comprises 300 hours in total, spread over 3 to 5 months. That is an average of 10 to 15 hours per week, compatible with full-time work.
The course uses Python (SHAP and LIME), IBM AI Fairness 360, Fairlearn and Jupyter Notebook. Free, academic or evaluation versions are used, and the tutor indicates the open-source alternative where one exists.
An SEIUM own diploma upon passing the continuous assessment and the integrative 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 recognised towards the advanced programme. Check the recognition policy when you enrol.
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