Pinpointing where poverty is most severe and tracking its changes over time is crucial for helping communities effectively. However, traditional benchmarks like household surveys and national censuses often fall short—they’re expensive, slow, and infrequent. In countries like Sri Lanka, this means we’re often relying on outdated information, hindering our ability to respond to sudden economic shocks or disasters. On top of that, poverty cannot be determined by income data alone, rather its multidimensional, where factors such as infrastructure, access to services, and economic activity also play a role in determining a community’s well-being. To capture these complexities, our DAP team (Data, Algorithms, and Policy) explored something different: how to rethink the way we measure poverty in Sri Lanka using AI with non-traditional data sources? 
Pinpointing where poverty is most severe and tracking its changes over time is crucial for helping communities effectively. However, traditional benchmarks like household surveys and national censuses often fall short—they’re expensive, slow, and infrequent. In countries like Sri Lanka, this means we’re often relying on outdated information, hindering our ability to respond to sudden economic shocks or disasters. On top of that, poverty cannot be determined by income data alone, rather its multidimensional, where factors such as infrastructure, access to services, and economic activity also play a role in determining a community’s well-being. To capture these complexities, our DAP team (Data, Algorithms, and Policy) explored something different: how to rethink the way we measure poverty in Sri Lanka using AI with non-traditional data sources?