Sunday, 14 September 2025

When Machines Watch the Road: Reflections on ITI’s AI Road‑Safety Pilot in Uttar Pradesh

I read with a mixture of hope and cautious curiosity about the announcement that ITI Limited, in partnership with mLogica, will pilot India’s first state government‑led AI‑based road safety project in Uttar Pradesh — an Intelligent Traffic Management System (AI‑ITMS) planned for Lucknow with a state allocation of around Rs 10 crore Current Affairs Today: 30 July 2025 and reported by industry press as approved by the transport ministry Transport ministry approves UP's AI-based project to improve road safety.

This is not just another government tender. For a country where road fatalities are an everyday tragedy, the idea of bringing AI to the junction of policy, engineering and human behaviour feels like an ethical obligation as much as a technological experiment. Yet the ethical obligation is two‑edged: technology can reduce harm, but it can also amplify existing inequalities, obscure accountability, and normalize surveillance if deployed without care.

Why this pilot matters — and why I am guardedly optimistic

Lives saved are the ultimate metric. A well‑designed AI‑ITMS can detect dangerous patterns (overspeeding, red‑light violations, vulnerable pedestrian hotspots), trigger faster emergency response, and inform targeted infrastructure fixes. Even small percentage reductions in fatalities scale into thousands of lives saved in India.
Indigenous capability and public sector leadership. ITI Limited’s involvement signals that state actors and public enterprises can own and operationalize critical civic AI systems rather than leaving them solely to private vendors. That matters for sovereignty, long‑term maintenance and local adaptation Current Affairs Today: 30 July 2025.
Experimental governance in a real city. Pilots — if genuinely experimental — create knowledge: which sensors work in Indian lighting and weather conditions, what kinds of false positives are common, and how citizens respond when enforcement becomes semi‑automated.

The human costs technology must not hide

I drive and walk in Indian cities often enough to feel the precariousness of our public streets. That lived fragility is what any tech solution must respect. My concerns are practical and philosophical:

Surveillance vs. safety. Cameras and computer vision systems are powerful. But every camera deployed is also a lens into citizens’ lives. We must avoid turning understandable safety goals into normalised mass surveillance.
Algorithmic bias and enforcement asymmetry. If an AI model is trained primarily on certain vehicle types, road geometries or behaviours, it may under‑detect risks in informal settlements, rural approaches or among non‑motorized users (pedestrians, cyclists, carts). That produces unequal protection.
Accountability and redress. When an AI flags a violation that leads to a fine, how does a citizen contest a false positive? Who is legally responsible — the vendor, the system integrator, the municipal authority? The project must align with existing legal frameworks (the pilot is expected to comply with the Motor Vehicles Act and related rules) and extend them where necessary Current Affairs Today: 30 July 2025.

A practical checklist I would ask the pilot team to publish

Transparency breeds trust. Before scaling, the pilot should publish a short, readable governance and evaluation plan that includes:

Data governance: what raw data are collected, retention periods, access controls, and whether identifiable video is stored or immediately anonymized.
Explainability: which classes of incidents the AI flags, the confidence thresholds used, and an accessible description of common failure modes.
Oversight and human‑in‑the‑loop rules: which decisions remain with humans (warnings, fines, emergency dispatch), and which are automated.
Metrics and public dashboard: live or periodic updates on evaluation metrics such as detection accuracy, false‑positive/negative rates, reduction in response times, and change in accidents and fatalities at pilot sites.
Redress mechanisms: a clear, low‑friction path for citizens to appeal automated actions.

How to measure success beyond headlines

If this project is to prove its value, the evaluation must be rigorous and not merely PR friendly. Useful indicators include:

Reduction in fatal and serious injuries at monitored intersections (measured in rolling 12‑month windows).
Reduction in emergency medical response times to accidents detected by the system.
Precision and recall of the system for key events (speeding, red‑light violation, pedestrian intrusion), with breakdowns by time of day and weather.
Equity measures: was the system equally effective across neighbourhoods and road types?
System uptime and maintenance costs, to judge operational sustainability.

Governance, law and the social contract

I am persuaded that technology will shape civic life in irreversible ways. That makes the legal and normative scaffolding essential. India has nascent debates over data protection, the Information Technology Act, and sectoral laws like the Motor Vehicles Act. Pilots must treat compliance as the floor, not the ceiling. They should proactively incorporate privacy‑by‑design, independent audits, and public consultation.

A civic AI deployed by a state must answer to its citizens — not the other way around. The project’s legitimacy will come from being accountable, auditable and demonstrably focused on harm reduction.

Beware of techno‑solutionism — but don’t mistake caution for pessimism

It is tempting to think that better sensors and models alone will fix road safety. They won’t. Roads are social systems: human behaviour, urban design, enforcement culture, ambulance capacity, and education all weave together. AI can improve diagnostics and enable targeted interventions, but it cannot replace humane policy and physical design changes.

That said, I am encouraged that the pilot exists. It is an opportunity for India to build a template — a mix of engineering, ethics, and public governance — that other cities and states can learn from. We should treat this pilot like an experiment in public science: openly reported, independently evaluated, and iterated upon.

A few modest prescriptions from my perspective

Publish a short, public evaluation plan and an initial privacy impact assessment before the pilot turns on cameras in public spaces.
Commit to third‑party audits (technical and ethical) at mid‑pilot and post‑pilot stages.
Invest in training for on‑ground traffic personnel so the technology complements, not replaces, human judgement.
Use the pilot to build local capacity — open standards, shared datasets (anonymized), and community engagement — so the gains become public goods rather than vendor lock‑ins.

Final thought

Technologies are mirrors. When we point powerful tools at public life, they reflect both our best intentions and our deepest structural weaknesses. I hope ITI Limited’s AI‑ITMS in Uttar Pradesh becomes an honest mirror: one that helps us see where our streets fail, shows us how to fix them, and forces us to choose a future where safety and dignity travel together.

Regards,
Hemen Parekh