Algorithmic targeting of social policies: fairness, accuracy, and distributed governance

Targeted social policies are the main strategy for poverty alleviation across the developing world. These include targeted cash transfers (CTs), as well as targeted subsidies in health, education, housing, energy, childcare, and others. Due to the scale, diversity, and widespread relevance of targeted social policies like CTs, the algorithmic rules that decide who is eligible to benefit from them---and who is not---are among the most important algorithms operating in the world today. Here we report on a year-long engagement towards improving social targeting systems in a couple of developing countries. We demonstrate that a shift towards the use of AI methods in poverty-based targeting can substantially increase accuracy, extending the coverage of the poor by nearly a million people in two countries, without increasing expenditure. However, we also show that, absent explicit parity constraints, both status quo and AI-based systems induce disparities across population subgroups. Moreover, based on qualitative interviews with local social institutions, we find a lack of consensus on normative standards for prioritization and fairness criteria. Hence, we close by proposing a decision-support platform for distributed governance, which enables a diversity of institutions to customize the use of AI-based insights into their targeting decisions.