Programme Parameters
Advanced Postgrad
3 Weeks (30 Hrs)
Online (e-LMS)
• Prior working knowledge of the field and comfort with core tools and quantitative reasoning.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This 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. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
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.
📋 Course Objectives
1. Put AI Enablement techniques to work on real datasets and case studies.
2. Assemble a documented case study that evidences your applied capability.
👥 Who Should Enroll?
• Master's and senior undergraduate students specializing in AI Enablement
• R&D engineers and working professionals applying AI Enablement in industry
• Academics and educators building research or teaching capacity in AI Enablement
🚀 Key Learning Outcomes
• Tangible, reproducible AI Enablement work to show supervisors or employers.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
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
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
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
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
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
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
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Fairness metrics |
| Covered Tool / Platform | trade-offs analysis |
| Covered Tool / Platform | AI ethics boards |
| Covered Tool / Platform | equity audits |
Frequently Asked Questions
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