Education

1. Ethical AI
IN REALWORLD ENGINEERING

2. What is Ethical AI?
AI systems that are fair, honest, and
safe for everyone
 Technology that respects human
rights and values
 AI that helps people without
causing harm
 Systems that are transparent - we
can understand how they work

3. Why Ethical AI Matters in Engineering ?
Real Impact on People's Lives:
Healthcare: AI helps diagnose diseases - errors
can be life-threatening
Transportation: Self-driving cars must make
safe decisions for everyone
Banking: AI decides who gets loans for homes
and businesses
Education: AI affects which students get
opportunities
The Engineer's Responsibility:
As engineers, we design these systems - so we must ensure they're fair and safe!

4. Real Problems with Unfair AI
Hiring Discrimination: Amazon's AI
hiring tool rejected women candidates
automatically
Healthcare Bias: Hospital AI system
favored white patients over Black
patients
Criminal Justice: COMPAS system
predicted Black defendants as "high
risk" twice as often.
The Problem:
These aren't theoretical - they actually happened and affected real people's lives!

5. Common Sources of AI Bias
Where Bias Comes From:
1. Biased Training Data
Historical data reflects past discrimination
Missing representation of certain groups
2. Biased Algorithms ⚙
The way we design the system introduces
unfairness
Wrong assumptions about what data means
3. Biased Teams
Lack of diversity in engineering teams
Limited perspectives in design process

6. Ethical AI Across Engineering
Disciplines
1. Civil Engineering:
Smart city systems that serve all neighborhoods equally
Traffic management that doesn't discriminate by area
2. Mechanical Engineering: ⚙
Robots that work safely around all people
Manufacturing AI that protects worker safety
3. Electrical Engineering: ⚡
Power grid AI that ensures fair energy distribution
Smart devices that protect user privacy
4. Computer Science:
Fair algorithms for social media and search
AI that helps rather than replaces human judgment

7. How to Build Ethical AI Systems
Step 1 : Build Diverse Teams
Step 2 : Use Fair Data
Step 3 : Make It Transparent
Step 4 : Test Continuously

8. Case Study - Smart City Traffic System
The Challenge: A city wants to use AI to manage traffic lights and reduce congestion
Potential Problems:
AI might prioritize wealthy neighborhoods
Could ignore pedestrian safety in certain areas
Ethical Solutions Applied:
Collect data
Include pedestrians and cyclists
Test the system
Results:
Better traffic flow for everyone
Safer streets in all neighborhoods

9. Tools and Benefits of Ethical AI
Helpful Tools:
IBM AI Fairness 360: Detects and reduces bias in datasets
Google What-If Tool: Tests different scenarios for fairness
Microsoft Fair learn: Builds fairer machine learning
models
Benefits for Everyone:
For Society: Equal opportunities, better healthcare, safer
systems
For Engineers: Build trust, avoid legal problems,
professional pride
For Companies: Avoid expensive mistakes, reach more
customers, attract talent
Key Message:
Ethical AI isn't just the right thing to do - it's also good business and leads to better engineering solutions!

10. Action Steps for Students
• What You Can Do Right Now
Take online courses in AI ethics (many are free!)
Practice with bias detection tools
• In Your Projects
Include diverse perspectives in team projects
Test your projects with different types of users
• In Your Future Career
Choose employers who value ethical AI practices
Speak up when you see unfair systems being developed
Key Takeaway:
"As engineers, we have the power and responsibility to build AI that benefits everyone. The
choices we make today will shape the world of tomorrow. Let's make sure it's a fair and just
world for all."