Ethics course in aerospace AI
About our
The Aerospace AI Ethics Course explores the moral dilemmas and social implications of artificial intelligence in the aerospace industry. It focuses on AI governance, algorithmic transparency, and accountability in the development and deployment of AI systems, covering topics such as algorithmic bias, data privacy, flight safety, and employment impact. It analyzes the current regulatory framework and ethical standards, promoting AI that is fair, equitable, and human-centered.
The course provides tools for assessing ethical risk and responsible decision-making in aerospace AI projects, from software design to the implementation of autonomous systems. Active participation in debates and case studies is encouraged, preparing participants to lead ethical initiatives and mitigate the risks associated with AI. The training contributes to the creation of a more sustainable and reliable aerospace sector, with a focus on social well-being and global security.
Ethics course in aerospace AI
- Format: Hybrid
- Duration: 3 months
- Hours: 300 H
- Language: English / Spanish
- Registration date: 23-10-2026
- Start date: 12-12-2026
- Available places: 2
320 $
Competencies and results
What you will learn
1. Ethical Principles in Aerospace Artificial Intelligence: A Comprehensive Course
- Identify and evaluate key ethical dilemmas related to the development and implementation of AI in aerospace applications.
- Explore the principles of transparency, accountability, fairness, privacy, and security in the context of aerospace AI.
- Understand the social and economic impact of aerospace AI, including job displacement, accessibility, and justice.
- Analyze case studies and practical examples of AI applications in the aerospace industry, highlighting relevant ethical considerations.
- Develop skills for ethical decision-making in the design, development, and deployment of aerospace AI systems.
- Become familiar with existing and emerging regulations, standards, and governance frameworks for aerospace AI.
- Participate in debates and discussions on the ethical implications of aerospace AI, fostering a critical understanding and a proactive approach.
2. Rotor Modeling and Performance: Analysis and Optimization
- Understand the aerodynamic phenomena of rotors, including finite element analysis (FEA).
- Master rotor simulation, considering the effects of flapping, lagging, and torsion.
- Evaluate rotor stability against whirl flutter and other critical vibration modes.
- Understand and apply optimization methods to improve rotor performance, including aerodynamic efficiency.
- Study rotor lifespan and fatigue models.
- Learn about the design and analysis of composite materials for rotors, including laminate sizing.
- Analyze joints and bonded joints in rotors using FEA.
- Implement techniques of damage tolerance for rotor damage assessment.
- Apply non-destructive testing (NDT) methods such as ultrasonic testing (UT), radiography (RT), and thermography for rotor inspection.
3. Comprehensive user-oriented design and validation (from modeling to manufacturing)
You will learn to integrate the entire product development process, from initial model conception to final validation, applying user-centered methodologies. You will develop skills in parametric design, ergonomics, simulation, sustainable materials, 3D visualization, and manufacturing management, ensuring efficient, safe solutions that meet current industry standards.
4. Ethics in Aerospace AI: Responsible Design, Development, and Deployment
- Fundamental principles of ethics in artificial intelligence (AI) and their relevance in the aerospace sector.
- Ethical frameworks and regulations applicable to the design, development, and deployment of AI systems in aeronautics.
- Identification and analysis of biases in AI data and algorithms, and strategies to mitigate them.
- Governance and responsibility in the use of aerospace AI, including transparency and accountability.
- Impact of AI on safety, privacy, and autonomy in aerospace applications.
- Emerging ethical challenges related to AI, such as the use of autonomous drones and weapons systems.
- Design of responsible AI systems, considering equity, inclusion, and the common good.
- Evaluation of ethical risks and development of risk management strategies in AI projects.
- Development of policies and codes of conduct for the ethical use of AI in the aerospace sector.
- Case studies and practical examples of the application of ethics in aerospace AI projects.
5. Ethical Foundations of Aerospace AI: An In-Depth Analysis
5. Ethical Foundations of Aerospace AI: An In-Depth Analysis
- Ethical principles in the development and deployment of AI in aerospace applications.
- Impact of AI on safety, accountability, and transparency in aerospace systems.
- Analysis of algorithmic biases and their mitigation in aerospace AI decision-making.
- Regulatory and normative framework for aerospace AI, including privacy and data protection.
- Case studies on ethical dilemmas in the use of AI in navigation, flight control, and air traffic management.
- Development of strategies for ethical decision-making in the design, implementation, and monitoring of aerospace AI systems.
- The future of aerospace AI: emerging ethical challenges and their impact on Innovation and society.
6. Ethics in Aerospace AI: Navigating the Future Responsibly
6. Ethics in Aerospace AI: Navigating the Future Responsibly
- Understand the fundamental principles of ethics applied to artificial intelligence (AI).
- Identify and analyze the specific ethical dilemmas that arise in the development and implementation of AI in the aerospace sector.
- Evaluate the social, economic, and environmental impact of aerospace AI, including issues such as privacy, security, and fairness.
- Explore regulatory frameworks and ethical standards for AI, and how these apply to aerospace applications.
- Develop strategies for ethical decision-making in the design, development, and deployment of aerospace AI systems.
- Analyze the role of transparency, responsibility, and accountability in aerospace AI.
- Evaluate the impact of AI on flight safety and Air traffic management.
Examine the use of AI in military applications and the associated ethical implications.
Explore the future of aerospace AI and the ethical challenges that could arise.
Develop skills for participating in ethical debates and promoting responsible and sustainable aerospace AI.
Who this program is for:
Ethics course in aerospace AI
- Graduates in Aerospace Engineering, Mechanical, Industrial, Automatics or similar.
- OEM rotorcraft/eVTOL professionals, MRO, consulting, technology centers.
- Flight Test, certification, avionics, control and dynamics who seek specialization.
- Regulators/authorities and UAM/eVTOL profiles that require compliance competencies.
- Standards-driven curriculum: you will work with CS-27/CS-29, DO-160, DO-178C/DO-254, ARP4754A/ARP4761, ADS-33E-PRF from the first module.
- Accreditable laboratories (EN ISO/IEC 17025) with rotor bench, EMC/Lightning pre-compliance, HIL/SIL, vibrations/acoustics.
- Evidence-oriented TFM: safety case, test plan, compliance dossierand operational limits.
- Mentored by industry: teachers with experience in rotorcraft, tiltrotor, eVTOL/UAM and flight test.
- Flexible modality (hybrid/online), international cohorts and support from SEIUM Career Services.
- Ethics and security: safety-by-design approach, cyber-OT, DIH and compliance as pillars.
2.1 Definition and Scope of Aerospace AI
2.2 Fundamental Ethical Principles: Beneficence, Non-Maleficence, Autonomy, and Justice
2.3 Specific Ethical Challenges: Bias, Privacy, Transparency, and Accountability
2.4 Regulatory Frameworks and Ethical Standards in AI
2.5 Importance of Ethics in the Design and Development of AI Systems
2.6 Case Studies: Examples of Ethical Dilemmas in Aerospace AI
2.7 Tools and Methodologies for Ethical Assessment
2.8 The Role of Stakeholders in Ethical AI
2.9 Future Trends and the Evolution of Ethics in Aerospace AI
2.10 Conclusion: The Need for a Responsible and Ethical Approach
2.2 eVTOL and UAM: Electric Propulsion, Multiple Rotors
2.2 Emerging Certification Requirements (SC-VTOL, Special Conditions)
2.3 Energy and Thermal in E-Propulsion (Batteries/Inverters)
2.4 Design for Maintainability and Modular Swaps
2.5 LCA/LCC in Rotorcraft and eVTOL (Footprint and Cost)
2.6 Operations & Vertiports: Airspace Integration
2.7 Data & Digital Thread: MBSE/PLM for Change Control
2.8 Tech Risk and Readiness: TRL/CRL/SRL
2.9 IP, Certifications, and Time-to-Market
2.20 Case Study: Go/No-Go with Risk Matrix
3.3 Introduction to AI in the Aerospace Sector and its Ethical Implications
3.2 General Ethical Framework: Relevant Principles, Values, and Regulations
3.3 Algorithmic Biases: Identification and Mitigation in AI Systems
3.4 Data Privacy and Security: Protecting Sensitive Information
3.5 Transparency and Explainability: Understanding How AI Works
3.6 Responsibility and Accountability in the Design and Use of AI
3.7 Human Rights and AI: Impact on Equality and Non-Discrimination
3.8 Ethical Governance of AI: Regulatory Frameworks and International Standards
3.9 Case Studies: Examples of Ethical Dilemmas in Aerospace AI
3.30 The Future of AI Ethics: Challenges and Opportunities
4.4 Foundations of Ethics in Aerospace AI Design
4.2 Ethical Principles in Data Collection and Use
4.3 Algorithmic Biases and Fairness in AI Systems
4.4 Transparency and Explainability in Decision-Making
4.5 Privacy and Security in Aerospace AI
4.6 Responsibility and Accountability
4.7 Ethical Considerations in the Development of Autonomous Systems
4.8 Social and Economic Impact of Aerospace AI
4.9 Regulatory Framework and Ethical Standards
4.40 Case Studies: Applying Ethics in Aerospace AI Projects
5. Fundamental Principles: Introduction to ethics in AI and the aerospace sector.
5. Regulations and Standards: Current and future legal and regulatory framework.
3. AI Governance: Policy design and oversight structures.
4. Transparency and Explainability: Methods for understanding and auditing AI systems.
5. Bias and Fairness: Identifying and mitigating bias in algorithms.
6. Data Privacy and Security: Protecting sensitive information.
7. Responsibility and Accountability: Defining responsibilities in the development and use of AI.
8. Social Impact: Ethical considerations of aerospace AI deployment.
9. Case Studies: Analysis of practical examples and ethical dilemmas.
50. The Future of Ethics in Aerospace AI: Emerging trends and challenges.
6.6 Biases in Aerospace AI Algorithms: Identification and Mitigation
6.2 Privacy and Data Protection in Autonomous Systems
6.3 Liability in Case of Failures and Accidents
6.4 Transparency and Explainability of Automated Decisions
6.5 Social and Economic Impact of Aerospace AI
6.6 Ethical Dilemmas in the Militarization of Space
6.7 Ethical Dilemmas Related to Aerial Surveillance and Monitoring
6.8 Responsible Use of Drones and Unmanned Aerial Vehicles
6.9 Governance and Regulation of Aerospace AI
6.60 Case Studies: Analysis of Specific Ethical Dilemmas
- Hands-on methodology: test-before-you-trust, design reviews, failure analysis, compliance evidence.
- Software (depending on licenses/partners): MATLAB/Simulink, Python (NumPy/SciPy), OpenVSP, SU2/OpenFOAM, Nastran/Abaqus, AMESim/Modelica, acoustics tools, planning toolchains DO-178C.
- SEIUM Laboratories: scale rotor bench, vibrations/acoustics, EMC/Lightning pre-compliance, HIL/SIL for AFCS, data acquisition with strain gauging.
- Standards and compliance: EN 9100, 17025, ISO 27001, GDPR.
Capstone-type projects
Admissions, fees, and scholarships
- Profile: Background in Computer Engineering, Mathematics, Statistics, or related fields; practical experience in NLP and valued information retrieval systems.
- Documentation: Updated CV, academic transcript, SOP/statement of purpose, project examples or code (optional).
- Process: Application → Technical evaluation of profile and experience → Technical interview → Review of case studies → Final decision → Enrollment.
- Fees:
- Single payment: 10% discount.
- Payment in 3 installments: No fees; 30% upon registration + 2 equal monthly payments of the remaining 35%.
Monthly payment: available with a 7% commission on the total; annual review.
Scholarships: based on academic merit, financial need, and promoting inclusion; agreements with companies in the sector for partial or full scholarships.
See “Calendar & Calls for Applications,” “Scholarships & Grants,” and “Fees & Financing” in the SEIUM mega-menu.
Do you have any questions?
Our team is ready to help you. Contact us, and we will respond as soon as possible.
F. A. Q
Frequently asked questions
Yes, we have international certification
Yes: experimental models, real data, applied simulations, professional environments, real case studies.
It is not mandatory. We offer leveling tracks and tutoring.
Completely. It covers e-propulsion, integration, and emerging regulations (SC-VTOL).
Recommended. There are also internal challenges and consortiums.
Yes. Online/hybrid modality with planned labs and visa support (see “Visa & Residence”).