Last week I committed to listening before proposing and to testing my assumptions against what I saw at eGov. This week, that approach led to a significant change in direction. Working with my team, we recognized that air pollution was an unusually high-impact place to ask the question at the centre of my project, which was how AI could serve communities that are often left out of technology decisions. Air quality is tied directly to health, justice, and equity, and it is an area where eGov's expertise in digital public infrastructure could matter. We decided to narrow the project to how AI could support air pollution mitigation.
The decision was exciting and also daunting, because it meant learning a new technical subject quickly. I spent most of the week reading. The scale of the problem was sobering. The average Indian is exposed to roughly 83 µg/m³ of fine particulate matter (PM2.5) every day, more than 16 times the World Health Organization's guideline, and long-term exposure caused an estimated 1.67 million deaths in 2019. India launched the National Clean Air Programme to bring 131 cities up to national standards, yet most have seen only modest improvements or worsening levels.
I also began learning how air quality is governed. Responsibility is spread across national, state, and city bodies, each with a different role in setting standards, monitoring, and enforcing rules. What stayed with me most was the equity dimension. Slum dwellers, migrant workers, small farmers, and Scheduled Castes and Tribes often live nearest to industrial sites, waste dumps, and busy roads, and they have the least access to healthcare. These are the same communities my original proposal set out to centre, so the new topic felt like a sharper version of the same purpose and not a departure from it.
The tangible outcome of this week was a solid foundation in the subject and a shared understanding with my supervisor of what the project would now try to produce: a brief on AI for air quality that could inform eGov's solutions development and its stakeholder mapping.
The Laidlaw attribute most visible this week was rigor and evidence-based thinking. Moving into an unfamiliar field made me want to check every claim and understand where each number came from. Adaptability and humility were also tested. Changing course meant accepting that part of my original plan would not happen as written, and that I had a lot to learn very quickly.
My role this week was still largely that of a student, but I began to see how I could contribute: by connecting a technical subject to the people it affects and by writing in a way that different audiences could use.
Next week, I will begin speaking with stakeholders who can show me how air quality decisions are actually made and where technology fits, so that my reading is tested against real experience.