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
This hands-on, technical-legal program bridges the gap between AI development and ethical governance, focusing on ensuring algorithmic fairness, avoiding discriminatory outcomes, and making AI decisions explainable to users, regulators, and stakeholders. Across 3 Weeks, you will work hands-on with ensuring algorithmic fairness and avoiding discriminatory outcomes, then consolidate everything in a capstone project. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This hands-on, technical-legal program bridges the gap between AI development and ethical governance, focusing on ensuring algorithmic fairness, avoiding discriminatory outcomes, and making AI decisions explainable to users, regulators, and stakeholders.
📋 Course Objectives
1. Build practical fluency in ensuring algorithmic fairness.
2. Gain working command of avoiding discriminatory outcomes.
3. Apply AI Enablement methods to authentic research and industry problems.
4. Build a defensible project you can showcase to supervisors, reviewers, or employers.
👥 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
• Data and computational scientists moving into ensuring algorithmic fairness
🚀 Key Learning Outcomes
• Confidence to apply ensuring algorithmic fairness in real projects.
• Confidence to implement avoiding discriminatory outcomes in real projects.
• A demonstrable AI Enablement project for your research or industry portfolio.
• 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 Bias in AI Systems
Identify sources of bias in datasets and models • Analyze social and ethical impacts of algorithmic bias • Examine case studies in healthcare, finance, and HR
Principles of Explainability and Interpretability
Understand why explainability matters in high-stakes AI • Distinguish between model transparency and post-hoc interpretability • Review regulatory expectations and standards
Bias Auditing in Practice
Apply fairness metrics and tools for bias auditing • Implement dataset balancing and preprocessing techniques • Mitigate bias during and after training
Explainability Techniques and Frameworks
Analyze feature importance and global model insights • Apply local interpretability methods like LIME, SHAP, and Anchors • Generate and present explanations to stakeholders
Governance, Ethics, and Documentation
Build ethical guardrails for AI systems • Create model cards and system fact sheets • Establish human-in-the-loop systems and review processes
Case Studies and Capstone
Examine bias and explainability in real products • Conduct a bias and explainability audit of a sample model • Present findings and remediation plans
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Aequitas |
| Covered Tool / Platform | IBM AI Fairness 360 |
| Covered Tool / Platform | Fairlearn |
| Covered Tool / Platform | What-If Tool |
| Covered Tool / Platform | LIME |
| Covered Tool / Platform | SHAP |
| Covered Tool / Platform | Anchors |
| Covered Tool / Platform | Counterfactual Explanations |
Frequently Asked Questions
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