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CourseDefense, Military Engineering & Land Systems Applications open

Course in Ethical Auditing Standards for Military AI

Online 300-hour course in ethical auditing standards for military AI. 6 modules covering NATO Principles for the Responsible Use of AI (2021), US DoD Directive 3000.09 (2023) and Python (scikit-learn). Assigned tutor and open enrolment.

Duration300 h
ModeOnline
Start datesSet by the faculty
AwardSEIUM Institute certificate
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Learning outcomes

What you will be able to do

01

Explain why AI systems in defence require specific auditing.

02

Compare the main international frameworks for responsible AI in defence.

03

Apply an auditing methodology based on ISO 19011.

04

Define technical criteria for assessing robustness, traceability and reliability.

05

Evaluate the governance and human oversight of a system.

06

Write audit findings supported by objective evidence.

07

Carry out a complete ethical audit of a military AI case.

Profile

Who it's for

  • AI auditor in defence who wants to specialise in Ethical Auditing Standards for Military AI.
  • Technology ethics adviser who wants to specialise in Ethical Auditing Standards for Military AI.
  • AI systems governance analyst who wants to specialise in Ethical Auditing Standards for Military AI.
  • Compliance manager in the defence industry who wants to specialise in Ethical Auditing Standards for Military AI.
  • AI ethics researcher who wants to specialise in Ethical Auditing Standards for Military AI.
  • Systems assurance consultant who wants to specialise in Ethical Auditing Standards for Military AI.
  • Engineering or science graduates seeking to enter defence administration.
Standards & tools

What you'll work with

  • NATO Principles for the Responsible Use of AI (2021)
  • US DoD Directive 3000.09 (2023)
  • Political Declaration on Responsible Military Use of Artificial Intelligence and Autonomy (2023)
  • ISO/IEC 42001:2023
  • ISO 19011:2018 (auditing guidelines)
  • Additional Protocol I (1977), Art. 36
  • ISO/IEC TR 24028 (AI trustworthiness)
Syllabus

6 modules · 300 h

  1. 1.1Applications of AI in defence
  2. 1.2Decision support, intelligence and logistics
  3. 1.3Risks specific to AI in military contexts
  4. 1.4Auditing versus assessment and certification
  5. 1.5Internal, external and third-party auditing
  6. 1.6Defence exclusion in Regulation (EU) 2024/1689
  7. 1.7NATO ethical principles for AI
  8. 1.8Ministry of Defence AI strategy
  9. 1.9Actors in the auditing ecosystem
  10. 1.10Case study: inventory of a unit's AI systems
  1. 2.1ISO/IEC 22989 concepts and terminology
  2. 2.2ISO/IEC 23894 AI risk management
  3. 2.3ISO/IEC 42001 AI management systems
  4. 2.4ISO/IEC 42005 impact assessment
  5. 2.5ISO/IEC 42006 requirements for auditors
  6. 2.6ISO/IEC TR 24027 bias in AI
  7. 2.7ISO/IEC 25059 quality of AI systems
  8. 2.8IEEE 7000 and ethical requirements
  9. 2.9NIST AI RMF 1.0 and its generative profile
  10. 2.10Workshop: mapping between ISO/IEC 42001 and NIST AI RMF
  1. 3.1ISO 19011 applied to AI systems
  2. 3.2Defining scope and criteria
  3. 3.3Evidence in machine learning systems
  4. 3.4Model cards and datasheets
  5. 3.5Interviews with developers and operators
  6. 3.6Sampling and reproduction of results
  7. 3.7Classification of findings
  8. 3.8Audit report and recommendations
  9. 3.9Follow-up of corrective actions
  10. 3.10Practical: audit plan for a decision support system
  1. 4.1Performance and validation metrics
  2. 4.2Robustness to distribution shift
  3. 4.3Adversarial attacks and robustness
  4. 4.4Fairness and data bias
  5. 4.5Explainability and interpretability
  6. 4.6Data and model traceability
  7. 4.7Human oversight and control
  8. 4.8Cybersecurity of the AI supply chain
  9. 4.9Monitoring in operation
  10. 4.10Laboratory: robustness assessment of an image classifier
  1. 5.1Responsibility and accountability
  2. 5.2IHL compliance and review under Article 36
  3. 5.3Ethics committees and their independence
  4. 5.4AI supplier management
  5. 5.5Transparency towards supervisory authorities
  6. 5.6Auditor independence and conflicts of interest
  7. 5.7Confidentiality and classified information
  8. 5.8Maturity of AI governance
  9. 5.9Ethical indicators and dashboards
  10. 5.10Case study: auditing the governance of an AI programme
  1. 6.1Selecting the system to be audited
  2. 6.2Scope and normative criteria
  3. 6.3Audit plan
  4. 6.4Technical evaluation of evidence
  5. 6.5Governance and oversight assessment
  6. 6.6Classification of findings
  7. 6.7Audit report
  8. 6.8Corrective action plan
  9. 6.9Dissertation and presentation
  10. 6.10Final review with the tutor
Methodology, software & labs

How you'll learn

A 100 % online programme 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), IBM AI Fairness 360, Microsoft Responsible AI Toolbox, advanced Excel, in free, academic or evaluation versions. Assessments are based on simulated audit procedures for a decision support system, documentary review of governance and a matrix for assessing ethical risks. Assessment is continuous, module by module, plus the final integrating project.

Accredited laboratories(EN ISO/IEC 17025) with a rotor test rig, EMC/Lightning pre-compliance, HIL/SIL, vibration/acoustics.
Evidence-driven Master's Thesis:safety case, test plan, compliance dossier and operational limits.
Industry-mentored:lecturers with experience in training, , eVTOL/UAM and flight test.
Flexible format(hybrid/online), international cohorts and support from SEIUM Career Services.
Ethics and safety:a safety-by-design approach, with OT cyber security, IHL and compliance as its pillars.
Capstone project

Build it end to end

Your final project follows the real engineering workflow, step by step, reviewed by your tutor.

Selecting the system to be audited

Explain why AI systems in defence require specific auditing.

Scope and normative criteria

Compare the main international frameworks for responsible AI in defence.

Audit plan

Apply an auditing methodology based on ISO 19011.

Technical evaluation of evidence

Define technical criteria for assessing robustness, traceability and reliability.

Governance and oversight assessment

Evaluate the governance and human oversight of a system.

Career opportunities

Where this takes you

AI auditor in defenceTechnology ethics adviserAI systems governance analystCompliance manager in the defence industryAI ethics researcherSystems assurance consultant
Admissions & scholarships

Requirements

  • No prior degree is required.
  • Internet connection and a computer capable of running the programme's software.
  • Sufficient level 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.

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FAQ

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.

The course works with Python (scikit-learn), IBM AI Fairness 360, Microsoft Responsible AI Toolbox and advanced Excel. Free, academic or evaluation versions are used, and the tutor indicates the open-source alternative where one exists.

An SEIUM in-house diploma upon passing the continuous assessment and the integrating project. It is institution-specific training, not an official qualification from the Ministry.

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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