Programme Parameters
Graduate / Intermediate
3 Weeks (30 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
Participants will explore how AI intersects with laws, including issues of accountability, liability for AI systems, intellectual property rights for AI-generated content, and the legal implications of AI decisions. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
Participants will explore how AI intersects with laws, including issues of accountability, liability for AI systems, intellectual property rights for AI-generated content, and the legal implications of AI decisions.
📋 Course Objectives
1. Get comfortable working with issues of accountability.
2. Apply AI Enablement methods to authentic research and industry problems.
3. 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
• Data and computational scientists moving into issues of accountability
🚀 Key Learning Outcomes
• Confidence to apply issues of accountability 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
Who Answers for an AI System
• Product liability, negligence and the gap when no human made the decision
• Developer, deployer and user responsibility allocated across the supply chain
• Contractual allocation, indemnities and warranty limits in AI procurement
Ownership of Inputs and Outputs
• Copyright in AI-generated content and the human-authorship requirement
• DABUS and the consistent refusal of AI inventorship across jurisdictions
• Training data, text and data mining exceptions and the pending litigation
Privacy and Protection
• GDPR rights over automated decision-making and profiling
• India's Digital Personal Data Protection Act and consent obligations
• Purpose limitation and the difficulty of erasure once a model is trained
Sector and Horizontal Rules
• EU AI Act risk tiers and the duties attaching to high-risk systems
• Sectoral regulation in medical devices, finance and employment screening
• Extraterritorial reach and why location of deployment governs
Compliance in an Organisation
• AI inventories, impact assessments and documented human oversight
• Evidence a regulator expects: testing records, not policy statements
• Discrimination exposure and the audit obligations already in force
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Westlaw |
| Covered Tool / Platform | LexisNexis |
| Covered Tool / Platform | Microsoft Office |
| Covered Tool / Platform | AI Legal Tools |
| Covered Tool / Platform | Contract Analysis Software |
Frequently Asked Questions
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