Course in Ethics of AI Use for Conflict Prediction
Online 300-hour course in ethics of AI use for conflict prediction. 6 modules covering Regulation (EU) 2024/1689 (AI Act), the Council of Europe Framework Convention on AI and Human Rights (2024) and Python (scikit-learn). Assigned tutor and enrolment open.
Course in Ethics of AI Use for Conflict Prediction — studying from Singapore
Singapore maintains a comprehensive defence sector through the Ministry of Defence and the Defence Science and Technology Agency, which develops military technologies and manages procurement. Engineering work covers land systems, ammunition, command and control, and simulation. Industry partners include ST Engineering, which supplies defence and security systems globally. Engineers here work within strict security and export control frameworks, often combining systems integration with reliability and safety engineering.
Fees for Singapore: S$1,060 (Singapore dollar), with instalments and employer sponsorship available.
Relevant bodies in Singapore
Typical roles
Questions from Singapore
How does defence engineering work in Singapore, and which bodies oversee it?
Singapore's defence engineering is largely centred on the Ministry of Defence (MINDEF) and DSTA, the defence procurement and development agency, along with the Singapore Armed Forces. Engineers may register with the Professional Engineers Board (PEB). SEIUM's qualifications are private, not state degrees, and not accredited by any national body, so recognition for defence roles depends on the employer or relevant authority.
Who employs defence and land systems engineers in Singapore?
Main employers include MINDEF, DSTA, the SAF, and defence industry companies including ST Engineering. Access to many roles requires security clearance and citizenship or residency rules, so check eligibility early. Employers focus on proven competence and clearance status. Your final project can illustrate technical capability, but it does not replace these requirements.
Is the course manageable alongside a job, and can employers fund it?
The programme is fully online and taught in English, with an assigned technical tutor. Plan for a heavy workload: 1,900 hours for a master's, 800 for a postgraduate diploma, or 300 for a specialist course. Fees are payable in instalments, and employer sponsorship is possible, which is common in defence-linked organisations, though approval is up to them.
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 data-driven approaches to conflict prediction.
Identify biases in data sources on violence and conflict.
Analyse the risks of misuse and self-fulfilling prophecies.
Apply AI risk management frameworks to predictive models.
Design governance and oversight mechanisms for prediction.
Assess responsible applications oriented towards prevention.
Develop an early-warning model with documented ethical safeguards.
Who it's for
- Data scientist in conflict analysis who wants to specialise in Ethics of AI Use for Conflict Prediction.
- Early-warning analyst who wants to specialise in Ethics of AI Use for Conflict Prediction.
- Researcher in peace studies who wants to specialise in Ethics of AI Use for Conflict Prediction.
- Algorithm auditor who wants to specialise in Ethics of AI Use for Conflict Prediction.
- Adviser on conflict prevention policy who wants to specialise in Ethics of AI Use for Conflict Prediction.
- Geospatial analyst who wants to specialise in Ethics of AI Use for Conflict Prediction.
- Graduates in engineering or science seeking to join international organisations.
What you'll work with
- Regulation (EU) 2024/1689 (AI Act)
- Council of Europe Framework Convention on AI and Human Rights (2024)
- UNESCO Recommendation on the Ethics of AI (2021)
- NIST AI RMF 1.0
- ISO/IEC TR 24027 (bias in AI systems)
- ISO/IEC 23894 (AI risk management)
- OCHA Data Responsibility Guidelines
6 modules · 300 h
1.1Conflict early-warning systems1.2ViEWS, ACLED and UCDP projects1.3Predictor variables: economic, political and climatic1.4Statistical and machine learning models1.5Natural language processing of news1.6Satellite and social media data1.7Uses: prevention, diplomacy and military planning1.8Ethics of predicting violence1.9Self-fulfilling prophecies and the effects of prediction1.10Case study: analysis of ViEWS forecasts
2.1Quality and coverage of event databases2.2Media bias and under-represented regions2.3Event coding and subjectivity2.4Data on vulnerable populations2.5Consent and open sources2.6Telephony and mobility data2.7Class imbalance in rare events2.8Temporal validation and data leakage2.9Evaluation metrics for forecasts2.10Laboratory: prediction model using UCDP data
3.1Using predictions to justify interventions3.2Stigmatisation of countries and communities3.3Surveillance of ethnic or political groups3.4Use by authoritarian regimes3.5Individual prediction of radicalisation3.6Manipulation and disinformation3.7Confidentiality versus transparency3.8Liability for erroneous predictions3.9Harms of false negatives3.10Case study: early warning before the Rwandan genocide
4.1ICRC principles on humanitarian data protection4.2OCHA guidelines on data responsibility4.3Participation of affected communities4.4Communicating uncertainty4.5Model explainability4.6Independent auditing4.7Open publication of models and data4.8Collaboration between academia, NGOs and defence4.9Responsibility to Protect (R2P)4.10Workshop: communication protocol for an early warning
5.1Prevention and preventive diplomacy5.2Anticipatory humanitarian planning5.3Climate, migration and conflict5.4Assessment of policy impacts5.5Data-informed peacekeeping5.6Ex post evaluation of forecasts5.7Lessons from Syria, the Sahel and Ukraine5.8Good practice indicators5.9Responsible academic research5.10Case study: anticipatory action based on forecasts
6.1Definition of the region and time horizon6.2Selection and analysis of data sources6.3Model construction and validation6.4Bias and limitations analysis6.5Assessment of misuse risks6.6Protocol for communicating results6.7Model governance framework6.8Recommendations for use6.9Report and presentation6.10Final review with the tutor
How you'll learn
Fully 100% online training on the SEIUM Institute 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 the course with work.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is Python (scikit-learn), R, Fairlearn, Tableau, QGIS, in free, academic or evaluation versions. Trials are studied through real procedures for retrospective evaluation of early-warning models, bias audits of conflict data and analysis of prediction cases. Assessment is continuous by module, plus the final integrating project.
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Describe data-driven approaches to conflict prediction.
Identify biases in data sources on violence and conflict.
Analyse the risks of misuse and self-fulfilling prophecies.
Apply AI risk management frameworks to predictive models.
Design governance and oversight mechanisms for prediction.
Where this takes you
Requirements
- No prior degree is required.
- Internet connection and a computer capable of running the programme's software.
- Sufficient command 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.
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 uses Python (scikit-learn), R, Fairlearn, Tableau and QGIS. Free, academic or evaluation versions are used, and the tutor indicates the free alternative where one exists.
A SEIUM Institute diploma upon passing the continuous assessment and the integrating 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 advanced programme. Check the recognition policy when you enrol.
Related programmes
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…
Fundamentals of Cybersecurity in Defence Course
Online 300-hour course in fundamentals of cybersecurity in defence. 6 modules covering the Esquema Nacional de Seguridad (Royal Decree 311/2022), ISO/IEC 27001 (information security) and Wireshark.…
Course in Ethics in Artificial Intelligence in Flight Systems
Online 300 h course in ethics in artificial intelligence in flight systems. 6 modules covering DO-178C (software in airborne systems), ARP4754B (development of aircraft and systems)…
More in Defense, Military Engineering & Land Systems
All Defense, Military Engineering & Land Systems programmes → · How to apply · Fees & financing · Scholarships