Reflecting the Past, Shaping the Future: Making AI Work for International Development
USAID (2018), 97 pp.
"This document aims to inform and empower those who may have limited technical experience as they navigate an emerging ML/AI landscape in developing countries. Donors, implementers, and other development partners should expect to come away with a basic grasp of common ML techniques and the problems ML is uniquely well-suited to solve. We will also explore some of the ways in which ML/AI may fail or be ill-suited for deployment in developing-country contexts. Awareness of these risks, and acknowledgement of our role in perpetuating or minimizing them, will help us work together to protect against harmful outcomes and ensure that AI and ML are contributing to a fair, equitable, and empowering future." (Introduction, page 5)
Roadmap: How to use this document, 6
MACHINE LEARNING: WHERE WE ARE AND WHERE WE MIGHT BE GOING, 10
ML and AI: What are they? -- How ML works: The basics -- Applications in development -- Case study: Data-driven agronomy and machine learning at the International Center for Tropical Agriculture -- Case study: Harambee Youth Employment Accelerator
MACHINE LEARNING: WHAT CAN GO WRONG? 36
Invisible minorities -- Predicting the wrong thing -- Bundling assistance and surveillance -- Malicious use -- Uneven failures and why they matter
HOW PEOPLE INFLUENCE THE DESIGN AND USE OF ML TOOLS, 44
Reviewing data: How it can make all the difference -- Model-building: Why the details matter -- Integrating into practice: It's not just "Plug and Play"
ACTION SUGGESTIONS: WHAT DEVELOPMENT PRACTITIONERS CAN DO TODAY, 66
Advocate for your problem -- Bring context to the fore -- Invest in relationships -- Critically assess ML tools
Looking forward: How to cultivate fair & inclusive ML for the future, 74
Quick reference: Guiding questions, 78
Appendix: Peering under the hood, 80
MACHINE LEARNING: WHERE WE ARE AND WHERE WE MIGHT BE GOING, 10
ML and AI: What are they? -- How ML works: The basics -- Applications in development -- Case study: Data-driven agronomy and machine learning at the International Center for Tropical Agriculture -- Case study: Harambee Youth Employment Accelerator
MACHINE LEARNING: WHAT CAN GO WRONG? 36
Invisible minorities -- Predicting the wrong thing -- Bundling assistance and surveillance -- Malicious use -- Uneven failures and why they matter
HOW PEOPLE INFLUENCE THE DESIGN AND USE OF ML TOOLS, 44
Reviewing data: How it can make all the difference -- Model-building: Why the details matter -- Integrating into practice: It's not just "Plug and Play"
ACTION SUGGESTIONS: WHAT DEVELOPMENT PRACTITIONERS CAN DO TODAY, 66
Advocate for your problem -- Bring context to the fore -- Invest in relationships -- Critically assess ML tools
Looking forward: How to cultivate fair & inclusive ML for the future, 74
Quick reference: Guiding questions, 78
Appendix: Peering under the hood, 80
