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Course in Ethics in Military Algorithm Auditing

Online course of 300 h in ethics in military algorithm auditing. 6 modules covering Additional Protocol I (1977), Art. 36, Guiding Principles of the GEG of the CCAC (2019) and Python (pandas). Assigned tutor and open enrolment.

Duration300 h
ModeOnline
Start datesOctober / March
LocationEurope (Madrid)
AwardSEIUM Institute diploma
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Learning outcomes

What you will be able to do

01

Describe the use of algorithms in defence systems.

02

Audit the quality, provenance and representativeness of data.

03

Evaluate the robustness and bias of machine learning models.

04

Supervise the behaviour of a system in operation.

05

Apply the international ethical and legal framework to algorithmic auditing.

06

Document findings using the transparency criteria of IEEE 7001.

07

Carry out a complete algorithmic auditing project.

Profile

Who it's for

  • Algorithm auditor wishing to specialise in Ethics in Military Algorithm Auditing.
  • Data quality engineer wishing to specialise in Ethics in Military Algorithm Auditing.
  • AI risk analyst wishing to specialise in Ethics in Military Algorithm Auditing.
  • Ethics adviser in defence technology wishing to specialise in Ethics in Military Algorithm Auditing.
  • Researcher in robust machine learning wishing to specialise in Ethics in Military Algorithm Auditing.
  • Compliance consultant wishing to specialise in Ethics in Military Algorithm Auditing.
  • Engineering or science graduates seeking to enter the defence industry.
Standards & tools

What you'll work with

  • Additional Protocol I (1977), Art. 36
  • Guiding Principles of the GEG of the CCAC (2019)
  • IEEE 7001-2021 (transparency of autonomous systems)
  • ISO/IEC 23894:2023
  • ISO/IEC TR 24027 (bias in AI)
  • ISO/IEC 25012 (data quality)
  • NATO Principles for the Responsible Use of AI (2021)
Syllabus

6 modules · 300 h

  1. 1.1Types of algorithms: rules, optimisation and learning
  2. 1.2Sensor fusion algorithms
  3. 1.3Target classification and recognition
  4. 1.4Logistics and maintenance planning
  5. 1.5Intelligence analysis and prioritisation
  6. 1.6Decision support systems
  7. 1.7Algorithm lifecycle
  8. 1.8Risks of opaque algorithms
  9. 1.9The need for algorithmic auditing
  10. 1.10Case study: analysis of a target recommendation system
  1. 2.1Data provenance and lineage
  2. 2.2Quality and representativeness
  3. 2.3Labelling and annotation errors
  4. 2.4Synthetic and simulated data
  5. 2.5Sampling biases in conflict contexts
  6. 2.6Class imbalance
  7. 2.7Protection of personal data
  8. 2.8Documentation with datasheets for datasets
  9. 2.9Data leakage between training and testing
  10. 2.10Workshop: auditing an image dataset
  1. 3.1Performance metrics and their interpretation
  2. 3.2Confusion matrices and asymmetric costs
  3. 3.3Calibration of model confidence
  4. 3.4Error analysis by subgroup
  5. 3.5Robustness to perturbations
  6. 3.6Evasion and poisoning attacks
  7. 3.7Detection of out-of-distribution inputs
  8. 3.8Explainability with SHAP, LIME and saliency maps
  9. 3.9AI red teaming
  10. 3.10Laboratory: error analysis and calibration of a model
  1. 4.1Integration of the algorithm into the system
  2. 4.2Operator interaction
  3. 4.3Automation bias
  4. 4.4Drift monitoring
  5. 4.5Logging and traceability of decisions
  6. 4.6Updates and version control
  7. 4.7Management of algorithmic incidents
  8. 4.8Supply chain cybersecurity
  9. 4.9Functional safety
  10. 4.10Practice: designing a monitoring plan
  1. 5.1IHL and decisions supported by algorithms
  2. 5.2Review of weapons and algorithms
  3. 5.3Responsibility of commanders and developers
  4. 5.4NATO and Ministry of Defence principles
  5. 5.5NIST AI RMF applied to defence
  6. 5.6ISO/IEC 42001 and auditing
  7. 5.7Auditor independence
  8. 5.8Confidentiality and access to information
  9. 5.9Communicating results
  10. 5.10Case study: ethical audit of a prioritisation algorithm
  1. 6.1Selecting the algorithm
  2. 6.2Scope and criteria
  3. 6.3Data auditing
  4. 6.4Model evaluation
  5. 6.5Evaluation in an operational context
  6. 6.6Ethical and legal analysis
  7. 6.7Audit report
  8. 6.8Improvement plan
  9. 6.9Dissertation and presentation
  10. 6.10Final review with the tutor
Methodology, software & labs

How you'll learn

Training is 100% online 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 enrolment period, 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 (pandas), Great Expectations, SHAP, Microsoft Responsible AI Toolbox, in free, academic or evaluation versions. Tests are studied using real procedures for auditing the quality of a dataset, robustness testing of a model and ethical review of a case study. Assessment is continuous by module, plus a final integrative project.

Accredited laboratories(EN ISO/IEC 17025) with a rotor test rig, EMC/Lightning pre-compliance, HIL/SIL, vibration/acoustics.
Evidence-focused Master's Thesis (final project):safety case, test plan, compliance dossier and operational limits.
Industry mentoring:lecturers with a track record 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 cyber-OT, DIH 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 algorithm

Describe the use of algorithms in defence systems.

Scope and criteria

Audit the quality, provenance and representativeness of data.

Data auditing

Evaluate the robustness and bias of machine learning models.

Model evaluation

Supervise the behaviour of a system in operation.

Evaluation in an operational context

Apply the international ethical and legal framework to algorithmic auditing.

Career opportunities

Where this takes you

Algorithm auditorData quality engineerAI risk analystEthics adviser in defence technologyResearcher in robust machine learningCompliance consultant
Admissions & scholarships

Requirements

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

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FAQ

Frequently asked questions

The programme consists of 300 hours in total, spread over 3 to 5 months. That is between 10 and 15 hours a week on average, compatible with a full-time job.

Work is carried out with Python (pandas), Great Expectations, SHAP, Microsoft Responsible AI Toolbox. Free, academic or evaluation versions are used, and the tutor indicates the free alternative where one exists.

An in-house diploma from SEIUM Institute upon passing the continuous assessment and the final integrative project. It is institutional 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. Check the credit transfer policy when you enrol.

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