Ethical Dimensions of AI in Engineering Education
Module 3 - Brazil Fulbright Workshop
2025-09-18
Welcome to Module 3
Ethical Dimensions of AI in Engineering Education
Building responsible AI practices in engineering pedagogy
Why Ethics Matter More in Engineering AI
- Scale of Impact: Engineering decisions affect public safety and welfare
- Professional Responsibility: Engineers have explicit ethical obligations
- Amplified Consequences: AI can scale both benefits and harms exponentially
- Student Preparation: Future engineers need ethical AI competencies
- Societal Trust: Public confidence in engineering depends on ethical practice
“With great computational power comes great responsibility”
Recent AI Ethics Incidents in Engineering
Autonomous Vehicles - Bias in pedestrian detection - Ethical decision algorithms - Liability questions
Structural Design AI - Optimization vs. safety margins - Material selection bias - Code compliance issues
Hiring Algorithms - Gender bias in tech recruiting - University ranking bias - Accessibility discrimination
Infrastructure AI - Traffic optimization inequity - Smart city surveillance - Environmental justice
The Five Pillars of AI Ethics in Engineering
1. Bias & Fairness 2. Privacy & Security 3. Transparency & Explainability 4. Accountability 5. Environmental Impact
Pillar 1: Bias & Fairness
The Challenge: - Training data reflects historical inequities - Algorithms can perpetuate or amplify bias - “Objective” optimization may not be equitable
Engineering Examples: - Bridge inspection AI missing certain corrosion types - Hiring algorithms favoring specific schools - Traffic optimization that disadvantages pedestrians
Key Questions: - Whose perspectives are represented? - Who benefits vs. who bears risks? - Are there systematic patterns of unfair treatment?
Pillar 2: Privacy & Security
The Challenge: - Engineering AI often uses sensitive data - Student privacy in educational applications - Proprietary information in design tools
Engineering Examples: - Student performance data in AI tutoring - Proprietary designs in AI-assisted CAD - Personal data in smart infrastructure
Key Questions: - What data is collected and how is it protected? - Who has access to sensitive information? - What are the risks if compromised?
Pillar 3: Transparency & Explainability
The Challenge: - “Black box” AI in critical decisions - Students need to understand AI reasoning - Professional liability requires explainability
Engineering Examples: - AI-recommended structural modifications - Automated grading of engineering work - AI-optimized manufacturing processes
Key Questions: - Can we understand how AI reached its decision? - Are limitations clearly communicated? - Can stakeholders evaluate the reasoning?
Pillar 4: Accountability
The Challenge: - Who is responsible when AI makes mistakes? - Human oversight vs. AI autonomy - Professional engineering liability
Engineering Examples: - AI-assisted design failures - Student responsibility for AI-generated work - Quality control in AI-augmented systems
Key Questions: - Who is responsible when AI errs? - How do we ensure human oversight? - What happens when AI and human judgment conflict?
Case Study Deep Dive
Small Group Analysis (15 minutes)
Your Task: 1. Read your assigned case study 2. Use the ethical analysis worksheet 3. Identify key stakeholders and tensions 4. Prepare a 30-second summary
Ethical Decision-Making Frameworks
Consequentialist - Focus on outcomes - Greatest good for greatest number - Cost-benefit analysis - Risk assessment
Deontological - Focus on duties/rules - Professional codes of ethics - Universal principles - Rights and obligations
Virtue Ethics - Focus on character - What would an exemplary engineer do? - Professional virtues - Role model behavior
Engineering Code of Ethics + AI
Traditional NSPE Principles: 1. Hold paramount public safety, health, and welfare 2. Perform services only in areas of competence 3. Issue public statements only in objective manner 4. Act for each employer/client as faithful agents 5. Avoid deceptive acts 6. Conduct themselves honorably and responsibly
AI Extensions: - Understand AI system limitations and communicate them clearly - Ensure AI recommendations are within your area of competence - Take responsibility for AI-assisted decisions - Protect data privacy and security in AI applications
Practical Decision Tree for Ethical AI Use
graph TD
A[Considering AI Use] --> B{Is it within my competence?}
B -->|No| C[Seek training or collaboration]
B -->|Yes| D{Are there bias/fairness concerns?}
D -->|Yes| E[Implement bias mitigation]
D -->|No| F{Is it transparent/explainable?}
F -->|No| G[Add explainability measures]
F -->|Yes| H{Are privacy/security adequate?}
H -->|No| I[Implement safeguards]
H -->|Yes| J{Is environmental impact acceptable?}
J -->|No| K[Consider alternatives]
J -->|Yes| L[Proceed with monitoring]Developing Ethical Guidelines
Collaborative Guideline Development (15 minutes)
Your Task: 1. Form groups of 8-10 (mix disciplines) 2. Develop 5-7 actionable ethical guidelines 3. Focus on practical implementation 4. Prepare for gallery walk sharing
Contact & Follow-Up
Workshop Materials: Available online after the session
Community of Practice: Join our ongoing discussion platform
Follow-Up Resources: Compiled guidelines and additional readings will be shared
Questions: Feel free to reach out during breaks or after sessions
Let’s continue building ethical AI practices in engineering education together!
