As the pace of change accelerates, it is clear that human understanding needs new tools. We decided to build, and test, tools and methodologies — and there is no greater test of human understanding than an Indian election.
Anthro.ai is an experiment in alchemy — between experienced communications specialists, data scientists, anthropologists and mathematicians; and between the human mind, our instincts, and immersive data streams.
We believe in the power of the human mind to connect the dots. No matter how complex the world gets, our brains continue to make sense of it for us. More often than not, how successfully we navigate complexity is a function of the dots we are able to see.
We have set out to build maps for a new age of discovery, where we travel across datascapes and discover human society in all its messiness and glory. But unlike the explorers of old, our journeys are respectful, friendly, and mindful of local laws and customs.
We don’t access data we are not supposed to (so no Facebook scraping); we pay for things we are supposed to (news feeds accessed via multiple paid services); and we are extraordinarily careful about being aware of usage.
As we built systems and methods and completed successful projects, we quickly realised that we needed to test them out at scale. Which brings us to Uttar Pradesh, India during this period of time — the Lok Sabha elections of 2019.
More than 900 million voters may participate in the largest exercise of democracy in the world. This isn’t just a large number of people, but a very diverse group of people. I won’t bore you with stats — you can look at the Wikipedia entry here.
We chose to focus on Uttar Pradesh, which elects 80 members of parliament — the most of any state. We have spent some time building out different types of maps. And we have decided to publish notes based on the patterns we are seeing.
These patterns are built on the back of hundreds of thousands of data points being ingested every day. Our systems are granular — working at the polling booth level.
We invite you to use our maps, read our notes, arrive at your own conclusions and talk to us about them.
These notes are an experiment in data-driven points of view. We are immersing ourselves in information screens and data patterns and allowing ourselves to connect dots. We emerge to write a note — like the one you’re reading — which is our best understanding at a given moment in time. We believe ourselves to be correct in the moment, but are happy to be proven wrong. In either case we learn and improve.