Keynote 4 - Alejandro Noriega Campero

Algorithmic Targeting of Social Policies: Fairness, Accuracy, and Distributed Governance 

Abstract: 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 wide-spread 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. 

 

Alejandro Noriega Campero graduated from a PhD in applied artificial intelligence at the MIT Media Lab, and a masters in Technology and Policy from the MIT Institute for Data, Systems, & Society (IDSS). The vision driving Alejandro’s career is to bridge academic breakthroughs in AI with their sensible application for the public good. He has led applied research projects with the United Nations’ Big Data initiative (Global Pulse), the national governments of Mexico, Colombia, Costa Rica, Panama, Andorra, and Saudi Arabia, as well as several partners in industry. He recently founded Prosperia Labs, a social impact spinoff developing AI solutions in the areas of poverty and health. He there leads a collaboration between MIT and the Inter-American Development Bank (IDB) focused on developing "AI Systems for the Fair and Efficient Targeting of Social Policies"—such as Conditional Cash Transfers—across the globe. He also leads the development of Human+Ai decision systems for massifying early detection of chronic diseases in developing countries.

 
 

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ICEDEG 2020
22 - 24 April 2020
Buenos Aires, Argentina

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