Course in Responsible AI Ethics in Defence
Online 300-hour course in responsible AI ethics in defence. 6 modules covering NATO Principles for the Responsible Use of AI (2021), US DoD Directive 3000.09 (autonomy in weapon systems) and Python (PyTorch). Assigned tutor and open enrolment.
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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
Explain the responsible AI principles adopted in the field of defence.
Apply responsible practices across the entire lifecycle of an AI system.
Identify technical risks such as bias, fragility and lack of robustness.
Analyse the role of human judgement and automation bias.
Examine legal responsibility and the review of new weapons.
Design oversight and accountability mechanisms.
Develop a responsible AI project for a defence use case.
Who it's for
- Responsible AI engineer wishing to specialise in Responsible AI Ethics in Defence.
- Technology ethics advisor in defence wishing to specialise in Responsible AI Ethics in Defence.
- AI risk analyst wishing to specialise in Responsible AI Ethics in Defence.
- Human factors specialist wishing to specialise in Responsible AI Ethics in Defence.
- Legal adviser in international humanitarian law wishing to specialise in Responsible AI Ethics in Defence.
- AI programme manager wishing to specialise in Responsible AI Ethics in Defence.
- Graduates in engineering or science seeking to enter the defence sector.
What you'll work with
- NATO Principles for the Responsible Use of AI (2021)
- US DoD Directive 3000.09 (autonomy in weapon systems)
- Additional Protocol I, Art. 36 (review of new weapons)
- ISO/IEC 42001 (AI management systems)
- ISO/IEC 23894 (AI risk management)
- IEEE 7000 (design with ethical considerations)
- Guiding Principles of the CCW GGE on Lethal Autonomous Weapons Systems (2019)
6 modules · 300 h
1.1Uses of AI in defence: support, logistics, intelligence and weapons1.2DoD Ethical Principles (2020)1.3NATO Principles for the Responsible Use of AI (2021)1.4Ministry of Defence AI Strategy (2023)1.5Legality, accountability and explainability1.6Reliability, governance and bias mitigation1.7Military ethics and the profession of arms1.8Differences from civilian AI1.9Defence exclusion in Regulation (EU) 2024/16891.10Case study: comparison of national principles
2.1Problem definition and operational need2.2Ethical requirements by design2.3Data management and quality2.4Model development and documentation2.5Testing, evaluation, verification and validation (TEVV)2.6Deployment and monitoring2.7Decommissioning and dismantling2.8Tools: DoD Responsible AI Toolkit2.9Defence Innovation Unit checklists2.10Practical exercise: applying DIU guidelines to a project
3.1Bias and fairness in military data3.2Robustness against adversaries3.3Fragility in unseen situations3.4Uncertainty and calibration3.5Errors in language models: hallucinations3.6Model supply chain security3.7Cybersecurity of AI systems3.8Interaction between multiple systems3.9Unintended escalation3.10Laboratory: robustness testing of a classifier
4.1Human control and levels of intervention4.2Calibrated trust and automation bias4.3Design of decision interfaces4.4Operator training4.5Individual and command responsibility4.6Moral burden and moral injury4.7Human–machine teams4.8Competency assessment4.9Safety culture and reporting4.10Case study: the 2003 Patriot incident
5.1Applicable international humanitarian law5.2Legal review under Article 365.3State and individual criminal responsibility5.4Records and auditability5.5Investigation of AI incidents5.6Transparency before parliament5.7Civil society participation5.8Confidence-building measures5.9Lessons from recent cases5.10Workshop: investigation plan for an AI incident
6.1Selection of the use case6.2Assessment of necessity and proportionality6.3Ethical and legal requirements6.4Data plan6.5TEVV design6.6Human control design6.7Monitoring plan6.8Risk assessment report6.9Report and presentation6.10Final review with the tutor
How you'll learn
Fully 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, which makes 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 (PyTorch), MLflow, IBM AI Fairness 360, Jira, in free, academic or evaluation versions. Trials are studied using real procedures for ethical reviews of use cases, lifecycle risk assessments and human–machine interaction studies. Assessment is continuous, module by module, plus a final integrative project.
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Explain the responsible AI principles adopted in the field of defence.
Apply responsible practices across the entire lifecycle of an AI system.
Identify technical risks such as bias, fragility and lack of robustness.
Analyse the role of human judgement and automation bias.
Examine legal responsibility and the review of new weapons.
Where this takes you
Requirements
- No prior qualification 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 comprises 300 total hours spread over 3 to 5 months. That is an average of 10 to 15 hours per week, compatible with full-time work.
The programme works with Python (PyTorch), MLflow, IBM AI Fairness 360, Jira. Free, academic or evaluation versions are used, and the tutor indicates the open-source alternative where one exists.
A SEIUM diploma upon passing the continuous assessment and the final 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-level programme. Check the recognition policy when you enrol.
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