Course in Ethics of Machine Learning Use for Surveillance
Online 300-hour course in the ethics of machine learning use for surveillance. 6 modules covering Regulation (EU) 2024/1689 (Artificial Intelligence Act), Regulation (EU) 2016/679 (GDPR) and Python (scikit-learn). Personal tutor assigned 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
Describe the machine learning techniques used in surveillance.
Analyse the ethical foundations and the rights affected by algorithmic surveillance.
Explain the obligations of the Artificial Intelligence Act for biometric identification systems.
Measure bias and technical reliability in recognition models.
Apply data protection and fundamental rights impact assessments.
Design governance safeguards and human oversight.
Prepare a complete ethical assessment of an AI-based surveillance system.
Who it's for
- Algorithm auditor wishing to specialise in Ethics of Machine Learning Use for Surveillance.
- Artificial intelligence ethics specialist wishing to specialise in Ethics of Machine Learning Use for Surveillance.
- Data protection officer wishing to specialise in Ethics of Machine Learning Use for Surveillance.
- Responsible data scientist wishing to specialise in Ethics of Machine Learning Use for Surveillance.
- Surveillance policy analyst wishing to specialise in Ethics of Machine Learning Use for Surveillance.
- AI compliance consultant wishing to specialise in Ethics of Machine Learning Use for Surveillance.
- Engineering or science graduates looking to enter public administration.
What you'll work with
- Regulation (EU) 2024/1689 (Artificial Intelligence Act)
- Regulation (EU) 2016/679 (GDPR)
- Directive (EU) 2016/680 (data protection in law enforcement)
- European Convention on Human Rights, Art. 8
- Council of Europe Convention 108+
- ISO/IEC 42001 (AI management systems)
- ISO/IEC 23894 (AI risk management)
6 modules · 300 h
1.1Types of surveillance: targeted and mass1.2Data sources: video, signals, networks and metadata1.3Facial recognition and biometrics1.4Life pattern analysis1.5Behavioural anomaly detection1.6Predictive surveillance1.7Open-source intelligence (OSINT)1.8Large-scale processing architectures1.9Military context versus domestic security1.10Workshop: data flow map of a surveillance system
2.1Privacy as a fundamental right2.2Autonomy, dignity and the chilling effect2.3Necessity and proportionality2.4Presumption of innocence and profiling2.5Algorithmic discrimination2.6Opacity and the right to explanation2.7Consent and its absence in surveillance2.8Floridi's information ethics2.9Feminist and decolonial perspectives2.10Case study: analysis of the Clearview AI case
3.1European Convention on Human Rights, Article 83.2ECtHR case law: Big Brother Watch and Zakharov3.3General Data Protection Regulation3.4Directive (EU) 2016/680 on law enforcement3.5European AI Act: prohibited practices3.6Real-time remote biometric identification3.7National security exemptions3.8Parliamentary and judicial oversight of intelligence3.9United States frameworks: FISA and Section 7023.10Practice: classifying systems under the AI Act
4.1Bias in training data4.2NIST FRVT evaluation and demographic differences4.3False positive rates and their consequences4.4Fairness metrics and their incompatibilities4.5Model drift in changing environments4.6Explainability in surveillance decisions4.7Robustness against evasion and manipulation4.8Independent algorithmic auditing4.9Documentation of models and data4.10Lab: measuring bias in a classifier
5.1Data protection impact assessment5.2Fundamental rights impact assessment5.3Privacy by design5.4Techniques: anonymisation, differential privacy and federated learning5.5Human oversight and accountability5.6Usage logs and auditing5.7Ethics committees and external oversight5.8Public transparency and statistics5.9Export of surveillance technologies (Regulation 2021/821)5.10Case study: impact assessment of a smart camera network
6.1Selection of the surveillance system under study6.2Technical description and data flow6.3Necessity and proportionality analysis6.4Bias and reliability assessment6.5Legal compliance analysis6.6Proposal of safeguards6.7Oversight and audit plan6.8Final ethical opinion6.9Report 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 timetable: 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 standard and applied exercises. The working software is Python (scikit-learn), Fairlearn, IBM AI Fairness 360, Jupyter Notebook, in free, academic or evaluation versions. Practical work covers real bias auditing procedures for classification models, fundamental rights impact assessments and analysis of automated surveillance cases. 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.
Describe the machine learning techniques used in surveillance.
Analyse the ethical foundations and the rights affected by algorithmic surveillance.
Explain the obligations of the Artificial Intelligence Act for biometric identification systems.
Measure bias and technical reliability in recognition models.
Apply data protection and fundamental rights impact assessments.
Where this takes you
Requirements
- No prior degree 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 an average of 10 to 15 hours per week, compatible with a full-time job.
You will work with Python (scikit-learn), Fairlearn, IBM AI Fairness 360 and Jupyter Notebook. Free, academic or evaluation versions are used, and the tutor indicates the free alternative where one exists.
A SEIUM Institute certificate of completion once you pass the continuous assessment and the integrative 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 can be credited towards the advanced programme. Please check the credit transfer policy when you enrol.
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