A decade ago, new technology promised to tip the scales in the fight against global poverty. An explosion of digital data — together with engineering advances, novel analytical approaches, and new options for mobile delivery of products, services, and information — heralded the arrival of an exciting new frontier in global development. But today, as the world faces overlapping crises, this optimism has been tempered by concerns about utility, equity, privacy, security, and misinformation, sparking a popular backlash against tech.
Evidence to Action (E2A) 2023: Realigning Tech for Social Impact took an honest look at the role that new technologies — from hardware innovations to advancements in digital data and service delivery platforms — have played in improving outcomes for people experiencing poverty, noting key “wins” as well as inherent limitations and trade-offs. We drew on rigorous evidence as well as the perspectives of public and private sector experts to deliver a nuanced take on the role of technology in designing, targeting, and measuring effective and inclusive responses to global poverty.
This in-person, half-day event was free and by registration only. E2A 2023 featured speakers from a range of disciplines and sectors through panel discussions, fireside chats, and presentations. Themed coffee chats and a hosted reception after the event provided networking opportunities for speakers and participants and stimulated further discussion with researchers and decision makers at the center of this moment.
CEGA regrets that it was unable to accommodate virtual participation or attendance for this in-person event.
Collecting High Frequency Data to Improve Power Quality | Noah Klugman
Tracking the Leakage of Development Goods Using iBeacon Technology | Muthoni Ng’ang’a
Conservation impact evaluation using remotely sensed data | Robert Heilmayr
Earth Observation for Agriculture and Food Security | Kathy Baylis
Publications and platforms
Machine learning and phone data can improve targeting of humanitarian aid | Josh Blumenstock
Combining Satellite Imagery and Machine Learning to Predict Poverty | Marshall Burke
Policy and Program Briefs
Data Science for Development | CEGA
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