AI STUDIES
Pre-COVID GIZ/ADB AI Study: This study examines the early adoption and impact of AI technologies before the pandemic, providing a baseline for understanding AI's role in the SP sectors.
Post-COVID GIZ/ADB AI Study: Building on the pre-COVID study, this research highlights the
shifts and accelerated adoption of AI for social protection due to the global
health crisis.
ISSA AI Study: This report explores the critical impact of responsible AI
deployment in social security organizations, emphasizing its potential to
enhance service delivery and operations. It also examines the technical and
non-technical challenges, such as infrastructure gaps, limited expertise, and
ethical concerns, that must be addressed for successful AI integration.
UN Study: Governing AI for Humanity
WB: COVID-time SP innovations -- mainly targeting -- in data-scarce environments:
OECD: G7 Toolkit for Artificial
Intelligence in the Public Sector
Additional Resources:
Along with the above studies,
I’m also including three additional documents that focus on the use of novel
data sources, where AI is also mentioned as one of the potential key
technologies and a guide for the right implementation of data protection in social
protection systems:
Study on Novel Data Sources: This document explores how innovative data sources can be leveraged
to enhance social protection mechanisms.
Toolkit on Novel Data Sources: A practical guide that provides tools and strategies for utilizing
emerging data streams, with novel data sources and AI as one of the highlighted
technologies.
Implementation Guide – Good Practices for Ensuring Data Protection and Privacy in Social Protection Systems: SPIAC-B Publication designed for professionals involved in developing and managing social protection systems, focusing on data protection and privacy standards, with practical solutions for handling technologies like biometrics, cloud computing, automated decision-making, and AI. It provides updated guidance on working with technology providers, cash transfers, and tackling big data challenges, offering real-world examples for policymakers, social workers, and development agencies.