One in three internet users worldwide are children. Yet the online world and complex machine learning algorithms that drive, direct, and govern children’s experiences have not been constructed with their needs in mind. The science of child development is typically left out of these equations. Worse, the algorithms that dictate and shape online experiences may amplify offline inequalities and place our most vulnerable children at risk.
Children represent an especially vulnerable population who are exposed to high levels of poverty and inequality, while also being dependent on adults to structure their experiences and opportunities in positive ways. Offline, lower versus higher income youth increasingly occupy two separate physical and social worlds. Neighborhoods and schools are increasingly segregated by both income and race.
Meanwhile, affluent youth are receiving increased parental investments of time, money and resources. The resulting “opportunity gap” is expected to amplify existing inequalities in years to come and a further widening of this gap in online spaces would be disastrous for many of our most vulnerable children.
This project responds to this call to action by bringing together an interdisciplinary team of leaders from machine learning, pediatrics, psychology, and cultural anthropology, educational technology, communication sciences, and computational biology to focus on AI as a potential amplifier of inequality and create a research, policy and communication agenda that will bring these issues to the forefront of scientific, policy, legal and public discussions. The goal is to ensure that large investments in AI are also positive investments in our children and their future.
With support from CIFAR, and in collaboration with UNICEF and the World Economic Forum, CLL is helping to convene workshops and conversations on these topics.
Project Leads: Mimi Ito and Candice Odgers