This USAID 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.
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Authors: Catherine Sutherland, Bahle Mazeka, Sibongile Buthelezi, Duduzile Khumalo and Patrick Martel
Can datafication increase the ‘visibility’ of informal settlements in South Africa, in the context of a national and local state that holds both progressive and repressive approaches towards informal settlements?
This case study explores a datafication process that has been in place for five years in an informal settlement in Durban, which has been established through an inclusionary, participatory data collection and production process. It examines how and when the data moves in the information value chain, and the implications this movement has for achieving rights-based, instrumental, structural and distributive justice. It argues that procedural and rights-based justice can be achieved to a certain extent through the construction of an inclusionary datafication process. However, instrumental, structural and distributive justice is dependent on how the interventionist and developmental state of South Africa engages with the data, and whether it takes it up in a meaningful way, thus enabling it to lead to fundamental shifts in discourses, approaches and practices towards informality.
The results reveal that the ‘governance and knowledge platforms’ that are built through the datafication process are more important and powerful at first, than the actual data itself. However, informal settlers and other data intermediaries, who have learnt how to engage data to secure the ‘right of informal settlers to the city’, have begun to use the data in interesting ways, acting as champions, and re-shaping citizens’ relations with the state. While this does not secure tangible changes in informal settlements, it begins to shift discourses and power relations, which is critical to informal settlement upgrading.
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Artificial intelligence (AI) technology is rapidly proliferating around the world. A growing number of states are deploying advanced AI surveillance tools to monitor, track, and surveil citizens to accomplish a range of policy objectives—some lawful, others that violate human rights, and many of which fall into a murky middle ground.
To provide greater clarity, Carnegie presents an AI Global Surveillance (AIGS) Index—representing one of the first research efforts of its kind. The index compiles empirical data on AI surveillance use for 176 countries around the world. It does not distinguish between legitimate and unlawful uses of AI surveillance. Rather, the purpose of the research is to show how new surveillance capabilities are transforming the ability of governments to monitor and track individuals or systems. It specifically asks:
- Which countries are adopting AI surveillance technology?
- What specific types of AI surveillance are governments deploying?
- Which countries and companies are supplying this technology?
Learn more about our findings and how AI surveillance technology is spreading rapidly around the globe.