| Attribute | Detail |
|---|---|
| Format | Online (e-LMS) |
| Level | Moderate |
| Duration | 3 Weeks |
| Certification | e-Certification + e-Marksheet |
| Fee | ₹10749 / $124 |
| Tools | Fairness metrics trade-offs analysis AI ethics boards equity audits |
About the AI Fairness and Social Impact Course
The "AI Fairness and Social Impact" program addresses the urgent need to design and deploy AI systems that do not exacerbate bias, discrimination, or inequality.
Participants will explore socio-technical frameworks for fairness, ethics, and accountability, learn to apply bias mitigation methods, evaluate disparate impact and equity trade-offs, and analyze real-world harms caused by opaque or poorly designed AI systems.
Program Highlights
• Comprehensive coverage of AI Fairness and Social Impact from fundamentals to advanced applications
• Hands-on projects and real-world case studies in AI
• Expert-curated curriculum aligned with current industry standards
• Access to recorded lectures and e-LMS platform for flexible, self-paced learning
• e-Certification and e-Marksheet upon successful completion
• Dedicated mentor support and interactive doubt-clearing sessions
• Practical experience with tools: Fairness metrics, trade-offs analysis, AI ethics boards, equity audits
• Career-oriented training for academic and professional growth in AI
Course Curriculum
Module 1: Understanding AI Fairness
- Define fairness in AI and understand its importance
- Analyze historical case studies of bias and harm in AI deployment
- Learn key concepts: group fairness, individual fairness, procedural fairness
Module 2: Metrics, Trade-offs, and Tensions
- Learn popular fairness metrics and when to use them
- Understand trade-offs between accuracy, fairness, and utility
- Analyze technical vs. contextual fairness
Module 3: AI in High-Stakes Domains
- Examine AI's impact on criminal justice, healthcare, and education
- Analyze labor market impacts and discrimination in hiring tools
- Discuss surveillance, policing, and AI at the margins
Module 4: Community Engagement and Participatory AI
- Learn about community engagement and participatory AI design
- Analyze impact assessments and community consultations
- Discuss building culturally responsive AI systems
Module 5: AI Governance for Fairness
- Examine policy responses and legislative proposals for AI fairness
- Learn about organizational governance: AI ethics boards and equity audits
- Discuss transparency, documentation, and accountability mechanisms
Module 6: Capstone & Social Impact Strategy
- Evaluate real-world AI systems for fairness and harm
- Develop a capstone project: social impact assessment of an AI system
- Learn to present findings to stakeholders
Tools, Techniques, or Platforms Covered
Fairness metrics trade-offs analysis AI ethics boards equity audits
Real-World Applications
- Apply AI Fairness and Social Impact skills directly to academic research, thesis work, and publications
- Build a professional portfolio showcasing practical AI competencies
- Solve industry-relevant problems using AI Fairness and Social Impact methodologies and tools
- Contribute to open-source projects and collaborative research in AI
- Prepare for competitive examinations, interviews, and professional certifications in AI
Who Should Attend & Prerequisites
- Industry-recognized e-Certification + e-Marksheet from NanoSchool
- Hands-on training with practical projects and industrial datasets
- Dedicated expert mentorship and doubt resolution
Certification
