Ethics in Predictive AI Applied to Security Course
Online 300-hour course in ethics in predictive AI applied to security. 6 modules covering Regulation (EU) 2024/1689 (Artificial Intelligence Act), Directive (EU) 2016/680 (data protection in law enforcement) and Python (scikit-learn). 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
Explain how predictive analytics works when applied to security.
Detect and measure bias in predictive models.
Apply the prohibitions and obligations of the AI Act to predictive systems.
Design transparency and human oversight mechanisms.
Assess the impact of predictive surveillance on freedoms.
Carry out fundamental rights impact assessments.
Critically evaluate a real or simulated predictive system.
Who it's for
- Algorithm auditor who wants to specialise in Ethics in Predictive AI Applied to Security.
- Data scientist in public security who wants to specialise in Ethics in Predictive AI Applied to Security.
- Data protection officer who wants to specialise in Ethics in Predictive AI Applied to Security.
- AI risk analyst who wants to specialise in Ethics in Predictive AI Applied to Security.
- Technology legal adviser who wants to specialise in Ethics in Predictive AI Applied to Security.
- Researcher in algorithmic fairness who wants to specialise in Ethics in Predictive AI Applied to Security.
- Engineering or science graduates looking to enter public security.
What you'll work with
- Regulation (EU) 2024/1689 (Artificial Intelligence Act)
- Directive (EU) 2016/680 (data protection in law enforcement)
- Organic Law 7/2021 (data protection in criminal matters)
- ISO/IEC TR 24027 (bias in AI systems)
- NIST SP 1270 (managing bias in AI)
- European Convention on Human Rights
- Council of Europe Convention 108+
6 modules · 300 h
1.1Predictive Policing: Hot Spots and Person-based Models 1.2 Algorithms: Predpol, Regression and Machine Learning 1.3 Input Data: Complaints, Arrests and Sensors 1.4 Data Feedback Loops 1.5 Public, Private and National Security 1.6 values in tension: Effectiveness, Liberty and Fairness 1.7 Presumption of Innocence and Prediction 1.8 Fundamental Rights affected 1.9 experiences in Chicago, Los Angeles and the UK 1.10 Case Study: Analysis of the Strategic Subject List Program
2.1Sources of bias in historical data2.2Fairness metrics: demographic parity and equal opportunity2.3Kleinberg's impossibility theorem2.4The COMPAS and ProPublica debate2.5Proxy variables and indirect discrimination2.6Auditing with Fairlearn and AIF3602.7Mitigation before, during and after training2.8Intersectional evaluation2.9Bias in facial recognition: the Gender Shades study2.10Laboratory: fairness audit of a risk model
3.1Regulation (EU) 2024/1689: prohibited practices3.2High-risk systems in law enforcement3.3Real-time remote biometric identification3.4Directive (EU) 2016/680 on police data protection3.5Organic Law 7/20213.6EU Charter of Fundamental Rights3.7Case law: the SyRI case in the Netherlands3.8Fundamental rights impact assessment3.9Supervision by supervisory authorities3.10Case study: risk classification of a police system
4.1Explainability for officers and citizens4.2Automation bias in users4.3Designing effective human oversight4.4Records and traceability of decisions4.5The right to challenge decisions4.6Transparency of private providers4.7Public algorithm registers4.8Citizen participation and democratic control4.9Continuous performance evaluation4.10Workshop: designing a human oversight protocol
5.1Mass surveillance and the chilling effect5.2Intelligent video surveillance and behaviour analysis5.3Social media and prediction of radicalisation5.4Social scoring and its prohibition5.5Security at major events5.6Borders and risk assessment systems: ETIAS5.7Data on minors and vulnerable groups5.8Proportionality and necessity5.9Local moratoria and bans5.10Case study: algorithmic video surveillance at a sporting event
6.1Description of the system and its context6.2Data analysis and sources of bias6.3Technical fairness audit6.4Fundamental rights impact assessment6.5Regulatory compliance analysis6.6Oversight and transparency design6.7Recommendations for deployment or rejection6.8Monitoring plan6.9Report 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. There are no fixed timetables: students progress at their own pace within the intake, which makes it possible to combine study with work.
Each module combines theory, guided reading of the original regulation and applied exercises. The working software is Python (scikit-learn), Fairlearn, AI Fairness 360, SHAP, in free, academic or evaluation versions. Case studies are based on real procedures for algorithmic bias audits, fundamental rights impact assessments and predictive policing analyses. Assessment is continuous across each 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.
Explain how predictive analytics works when applied to security.
Detect and measure bias in predictive models.
Apply the prohibitions and obligations of the AI Act to predictive systems.
Design transparency and human oversight mechanisms.
Assess the impact of predictive surveillance on freedoms.
Where this takes you
Requirements
- No prior degree 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 is 300 hours in total, spread over 3 to 5 months. That is an average of 10 to 15 hours a week, which is compatible with a full-time job.
The programme uses Python (scikit-learn), Fairlearn, AI Fairness 360 and SHAP. 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 qualification.
Yes. Modules passed in a course or postgraduate diploma in the same area are recognised towards the higher programme. Check the recognition policy when you enrol.
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