IIT-Madras, Google, and Digital Futures Lab launch capacity-building program on responsible AI for officials
Indian Institute of Technology-Madras, in partnership with Google and Digital Futures Lab, announced a capacity-building program for government officials focused on building, procuring, and scaling responsible AI systems. The announcement was made during the "Strengthening Human Capital in AI Era" conclave at IIT-M.
The program will help officials work with AI confidently and safely: understand foundational technologies, assess real use cases, and apply responsible practices aligned with India's policy framework. It also emphasizes identifying risks, limits, and governance needs in real deployments.
Why this matters for government
- Procure AI with clarity: define outcomes, assess vendors, and avoid black-box solutions.
- Scale what works: move from pilot to production with standards, documentation, and monitoring.
- Protect citizens: apply responsible AI guardrails, bias checks, and privacy-by-design.
- Meet policy expectations: align with national guidance and departmental compliance requirements.
What the program covers
- Foundational AI concepts and where they fit in public service delivery.
- Evaluating AI solutions for procurement and scale, including data needs and performance metrics.
- Responsible AI practices aligned with India's policy framework.
- Risk, limitation, and governance checkpoints for real-world deployments.
Who should consider attending
- Department and program heads planning AI initiatives.
- Procurement and finance officials drafting RFPs and contracts.
- Policy, legal, and compliance teams setting guardrails.
- IT and data leaders responsible for deployment and oversight.
Practical outcomes you can expect
- A clearer view of which use cases are ready for AI and which are not.
- A checklist for evaluating vendors, models, data, and monitoring plans.
- Templates for governance, risk logging, and escalation paths.
How departments can prepare now
- List top 3 use cases tied to policy outcomes (e.g., service delivery, grievance redressal, inspections).
- Map data availability, quality, and access controls for each use case.
- Draft RFP criteria that require transparency, auditability, model updates, and exit plans.
- Plan a small pilot with clear metrics, then scale based on evidence.
Related resources
Upskilling options
If you're planning team-wide AI training paths by job role, explore curated options here: Courses by Job Role.
This initiative sets a clear path: build capability, buy responsibly, and scale with accountability. For public sector leaders, it's a timely way to convert AI interest into safe, measurable outcomes.
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