Protecting Children’s Privacy at Home, at School, and Everywhere in Between
Summary
Young people today face surveillance unlike any previous generation, at home, at school, and everywhere in between. Constant use of technology while their brains are still developing makes them uniquely vulnerable to privacy harms, including identity theft, cyberbullying, physical risks, algorithmic labeling, and hyper-commercialism. A lack of privacy can ultimately lead children to self-censor and can limit their opportunities. Already-vulnerable populations—who have fewer resources, less digital literacy, or are non-native English speakers—are most at risk.
Congress and the Federal Trade Commission (FTC) have repeatedly considered efforts to better protect children’s privacy, but the next administration must ensure that this is a priority that is actually acted upon by supporting strong privacy laws and providing additional resources and authority to the FTC and support to the Department of Education (ED). The Biden-Harris administration should also establish a task force to explore how to best support and protect students. And the FTC should use its current authority to increase its understanding of the children’s technology market and robustly enforce a strong Children’s Online Privacy Protection Act (COPPA) rule.
AI’s high-stakes outputs directly shape American economic, industrial, and technological outcomes. They must not be accepted entirely on the basis of institutional faith rather than verifiable evidence.
The clearinghouse will also neutrally mediate the assignment of auditors to audits—strengthening independence, trust, and ethical governance across the AI ecosystem.
Soft law was never meant to be a permanent solution. Treating it as one, and letting the sandcastle stand in for the skyscraper indefinitely, is how we end up with a decade of voluntary commitments and no enforceable accountability to show for it.
Jobseekers, employees at smaller organizations, and local public workers need more than a weekend crash course in AI. They need practice choosing useful tasks, protecting data, testing outputs, and explaining where human judgment remains necessary.