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CourseDefense, Military Engineering & Land Systems Applications open

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.

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

Describe data-driven approaches to conflict prediction.

02

Identify biases in data sources on violence and conflict.

03

Analyse the risks of misuse and self-fulfilling prophecies.

04

Apply AI risk management frameworks to predictive models.

05

Design governance and oversight mechanisms for prediction.

06

Assess responsible applications oriented towards prevention.

07

Develop an early-warning model with documented ethical safeguards.

Profile

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.
Standards & tools

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
Syllabus

6 modules · 300 h

  1. 1.1Conflict early-warning systems
  2. 1.2ViEWS, ACLED and UCDP projects
  3. 1.3Predictor variables: economic, political and climatic
  4. 1.4Statistical and machine learning models
  5. 1.5Natural language processing of news
  6. 1.6Satellite and social media data
  7. 1.7Uses: prevention, diplomacy and military planning
  8. 1.8Ethics of predicting violence
  9. 1.9Self-fulfilling prophecies and the effects of prediction
  10. 1.10Case study: analysis of ViEWS forecasts
  1. 2.1Quality and coverage of event databases
  2. 2.2Media bias and under-represented regions
  3. 2.3Event coding and subjectivity
  4. 2.4Data on vulnerable populations
  5. 2.5Consent and open sources
  6. 2.6Telephony and mobility data
  7. 2.7Class imbalance in rare events
  8. 2.8Temporal validation and data leakage
  9. 2.9Evaluation metrics for forecasts
  10. 2.10Laboratory: prediction model using UCDP data
  1. 3.1Using predictions to justify interventions
  2. 3.2Stigmatisation of countries and communities
  3. 3.3Surveillance of ethnic or political groups
  4. 3.4Use by authoritarian regimes
  5. 3.5Individual prediction of radicalisation
  6. 3.6Manipulation and disinformation
  7. 3.7Confidentiality versus transparency
  8. 3.8Liability for erroneous predictions
  9. 3.9Harms of false negatives
  10. 3.10Case study: early warning before the Rwandan genocide
  1. 4.1ICRC principles on humanitarian data protection
  2. 4.2OCHA guidelines on data responsibility
  3. 4.3Participation of affected communities
  4. 4.4Communicating uncertainty
  5. 4.5Model explainability
  6. 4.6Independent auditing
  7. 4.7Open publication of models and data
  8. 4.8Collaboration between academia, NGOs and defence
  9. 4.9Responsibility to Protect (R2P)
  10. 4.10Workshop: communication protocol for an early warning
  1. 5.1Prevention and preventive diplomacy
  2. 5.2Anticipatory humanitarian planning
  3. 5.3Climate, migration and conflict
  4. 5.4Assessment of policy impacts
  5. 5.5Data-informed peacekeeping
  6. 5.6Ex post evaluation of forecasts
  7. 5.7Lessons from Syria, the Sahel and Ukraine
  8. 5.8Good practice indicators
  9. 5.9Responsible academic research
  10. 5.10Case study: anticipatory action based on forecasts
  1. 6.1Definition of the region and time horizon
  2. 6.2Selection and analysis of data sources
  3. 6.3Model construction and validation
  4. 6.4Bias and limitations analysis
  5. 6.5Assessment of misuse risks
  6. 6.6Protocol for communicating results
  7. 6.7Model governance framework
  8. 6.8Recommendations for use
  9. 6.9Report and presentation
  10. 6.10Final review with the tutor
Methodology, software & labs

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.

Accredited laboratories(EN ISO/IEC 17025) with 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 mentoring: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:safety-by-design approach, OT cyber, DIH and compliance as core pillars.
Capstone project

Build it end to end

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

Definition of the region and time horizon

Describe data-driven approaches to conflict prediction.

Selection and analysis of data sources

Identify biases in data sources on violence and conflict.

Model construction and validation

Analyse the risks of misuse and self-fulfilling prophecies.

Bias and limitations analysis

Apply AI risk management frameworks to predictive models.

Assessment of misuse risks

Design governance and oversight mechanisms for prediction.

Career opportunities

Where this takes you

Data scientist in conflict analysisEarly-warning analystResearcher in peace studiesAlgorithm auditorAdviser on conflict prevention policyGeospatial analyst
Admissions & scholarships

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.

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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 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.

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