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.
Course in Ethics in Military Algorithm Auditing — studying from Australia
Defence is one of Australia's largest engineering employers, with major programmes in land systems, naval platforms, aircraft and integrated systems managed through the Department of Defence. Land systems engineering supports armoured vehicles, weapons and logistics equipment, often through prime contractors and local industry partners. Industry involvement is strongly encouraged through the Defence industry policy framework. Engineers work on sustainment, capability development, systems integration and safety assurance, typically in Canberra, Adelaide, Melbourne and regional defence precincts.
Fees for Australia: A$868 (AUD), with instalments and employer sponsorship available.
Relevant bodies in Australia
Typical roles
Questions from Australia
How does defence engineering recognition work in Australia?
Defence engineering in Australia is largely run through the Department of Defence and its industry partners, which set their own security clearance and qualification requirements. Engineers Australia is the professional body for accreditation. SEIUM's private qualifications are not accredited by these bodies, so recognition is for employers or the relevant body to decide.
Which Australian employers hire in defence and land systems?
Australia has defence prime contractors, the Department of Defence, and specialist land systems firms. Many of these roles require Australian security clearances, which depend on citizenship and background checks rather than course completion. Ask employers how they weigh private qualifications against experience and clearance requirements.
Is the course manageable with a job, and does it need a visa?
The programmes are 100% online and taught in English, so you can study from Australia without a student visa. Specialist courses take 300 hours, diplomas 800 and master's 1,900. An assigned technical tutor supports you, fees are payable in instalments, and employer sponsorship may be possible.
Take the controls.
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
Describe the use of algorithms in defence systems.
Audit the quality, provenance and representativeness of data.
Evaluate the robustness and bias of machine learning models.
Supervise the behaviour of a system in operation.
Apply the international ethical and legal framework to algorithmic auditing.
Document findings using the transparency criteria of IEEE 7001.
Carry out a complete algorithmic auditing project.
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.
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)
6 modules · 300 h
1.1Types of algorithms: rules, optimisation and learning1.2Sensor fusion algorithms1.3Target classification and recognition1.4Logistics and maintenance planning1.5Intelligence analysis and prioritisation1.6Decision support systems1.7Algorithm lifecycle1.8Risks of opaque algorithms1.9The need for algorithmic auditing1.10Case study: analysis of a target recommendation system
2.1Data provenance and lineage2.2Quality and representativeness2.3Labelling and annotation errors2.4Synthetic and simulated data2.5Sampling biases in conflict contexts2.6Class imbalance2.7Protection of personal data2.8Documentation with datasheets for datasets2.9Data leakage between training and testing2.10Workshop: auditing an image dataset
3.1Performance metrics and their interpretation3.2Confusion matrices and asymmetric costs3.3Calibration of model confidence3.4Error analysis by subgroup3.5Robustness to perturbations3.6Evasion and poisoning attacks3.7Detection of out-of-distribution inputs3.8Explainability with SHAP, LIME and saliency maps3.9AI red teaming3.10Laboratory: error analysis and calibration of a model
4.1Integration of the algorithm into the system4.2Operator interaction4.3Automation bias4.4Drift monitoring4.5Logging and traceability of decisions4.6Updates and version control4.7Management of algorithmic incidents4.8Supply chain cybersecurity4.9Functional safety4.10Practice: designing a monitoring plan
5.1IHL and decisions supported by algorithms5.2Review of weapons and algorithms5.3Responsibility of commanders and developers5.4NATO and Ministry of Defence principles5.5NIST AI RMF applied to defence5.6ISO/IEC 42001 and auditing5.7Auditor independence5.8Confidentiality and access to information5.9Communicating results5.10Case study: ethical audit of a prioritisation algorithm
6.1Selecting the algorithm6.2Scope and criteria6.3Data auditing6.4Model evaluation6.5Evaluation in an operational context6.6Ethical and legal analysis6.7Audit report6.8Improvement plan6.9Dissertation and presentation6.10Final review with the tutor
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.
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Describe the use of algorithms in defence systems.
Audit the quality, provenance and representativeness of data.
Evaluate the robustness and bias of machine learning models.
Supervise the behaviour of a system in operation.
Apply the international ethical and legal framework to algorithmic auditing.
Where this takes you
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.
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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