1. Why AI Ethics Matters
AI has immense potential but also risks. Responsible AI ensures that systems are designed and deployed in ways that are fair, transparent, and safe. Unethical AI can lead to bias, misinformation, or harm to society.
Example: A recruitment AI that filters candidates must not discriminate based on gender, ethnicity, or age. Ethical design avoids unfair outcomes.
2. Core Principles of Ethical AI
- Fairness: Avoid bias in data and models.
- Transparency: Make AI decision processes understandable.
- Accountability: Have mechanisms to audit AI actions.
- Privacy: Protect user data.
- Safety: Prevent unintended harm from AI actions.
Responsible AI = Technology + Values + Governance.
3. Tools & Frameworks
- AI Fairness 360 (IBM) — bias detection in datasets and models
- Explainable AI (XAI) tools — interpret model decisions
- Privacy-preserving ML — federated learning, differential privacy
- Ethics checklists for ML deployment
4. Practical Example
Scenario: You are designing a chatbot for mental health advice.
Considerations: Avoid giving harmful advice, anonymize user data, and allow human oversight.
Use RAG + AI Ethics guidelines to provide safe, responsible responses.
5. Exercise
Review a dataset and identify potential biases.
Write a plan on how to mitigate these biases in an AI model.
Think about fairness, transparency, and privacy.
6. Inspirational Quote
"Technology is powerful, but ethics gives it purpose." — AI Wisdom