Direction 01
A skills passport that verifies what a worker can actually do through structured, employer-recognised challenges
Sustainable Development Goal 08 / 17
Decent Work and Economic Growth
Promote sustained, inclusive economic growth, full and productive employment and decent work for all. For builders: tools that connect skills to opportunity fairly and give informal workers real protections.
Why this goal matters
Most working people in India are informal: no contracts, no visibility of rights, no portable work history. Meanwhile employers say they cannot find skilled people. Between these two facts sits an information and trust gap that well-built software can narrow — matching honestly, verifying fairly and making rights legible.
The Indian context
India adds millions of job-seekers to its workforce every year while the gig economy rewrites what employment means. Skill-certification programmes exist but signals are noisy; migrant workers lose their work reputation every time they move; and micro-entrepreneurs — tailors, mechanics, food-stall owners — run real businesses with zero business tooling.
Tamil Nadu, specifically
Tamil Nadu is one of India’s most industrialised states — automotive belts around Chennai, the pump and foundry cluster of Coimbatore, and Tiruppur’s knitwear economy. Each cluster has specific skill shortages and each employs large migrant and female workforces whose work histories and rights-awareness deserve better infrastructure.
People and communities affected
- Gig and platform workers without income stability
- Migrant workers with non-portable reputations
- First-time job seekers from non-elite colleges
- Micro-entrepreneurs without business tooling
- MSME owners who cannot find verified skilled labour
Key challenge areas
- Honest skill-to-job matching
- Portable work-history and reputation
- Gig-worker income planning and protections
- Micro-enterprise operations tooling
- Labour-rights awareness in local languages
The AI Lens
Where AI belongs here — and where it does not.
Where AI can meaningfully help
- Matching real demonstrated skills — not keyword résumés — to real job requirements
- Interview and portfolio preparation feedback for first-generation job seekers
- Simple-language answers on wages, PF and grievance routes, grounded in actual law
- Demand forecasting for micro-entrepreneurs deciding stock and pricing
Where AI may be inappropriate
- Opaque automated hiring rejections — candidates deserve reasons and appeal paths
- Worker-surveillance productivity scoring
- Wage-suppressing algorithmic management practices
Build Directions
Example problem directions
Starting points, not prescriptions — the strongest submissions narrow one of these into a specific, evidenced local problem.
Direction 02
A gig-income planner that smooths volatile earnings into a realistic weekly budget
Direction 03
A Tamil-first labour-rights assistant grounded in the actual acts and state rules
Direction 04
A micro-business copilot for pricing, stock and simple accounts, voice-first
Potential users
- Job seekers and gig workers
- MSME and cluster employers
- Industrial-training institutes
- Worker-welfare organisations
Possible datasets
- Public labour-law texts and government scheme documents
- PLFS aggregate employment indicators (public)
- NCS and skill-mission published data
- Consented pilot data from a specific cluster or campus
Data-access limitations
- Informal-sector reality is undocumented by definition — expect to gather primary data
- Job-posting datasets are noisy and duplicated
- Skill taxonomies age quickly; design for update
Privacy, bias and safety risks
- Matching systems can encode caste, gender and college bias — audit for it explicitly
- Wrong legal guidance harms workers; ground and cite every claim
- Reputation systems can become blacklists; workers need correction rights
Responsible-AI considerations
- Publish your matching criteria in plain language
- Give every automated decision a human-readable reason and appeal path
- Audit outcomes across gender and background groups in your pilot
Suggested impact metrics
- Verified placements or gigs obtained through the tool
- Match-quality: retention or satisfaction after 30 days, not just clicks
- Bias audit deltas across demographic slices in pilot data
Related SDGs
Related challenge areas
Participant resources
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