An AI-based computer system can gather data and use that data to make decisions or solve problems – using algorithms to perform tasks that, if done by a human, would be said to require intelligence. The benefits created by AI and machine learning (ML) systems for better health care, safer transportation, and greater efficiencies across the globe are already happening. But the increased amounts of data and computing power that enable sophisticated AI and ML models raise questions about the privacy impacts, ethical consequences, fairness, and real world harms if the systems are not designed and managed responsibly. FPF works with commercial, academic, and civil society supporters and partners to develop best practices for managing risk in AI and ML and assess whether historical data protection practices such as fairness, accountability, and transparency are sufficient to answer the ethical questions they raise.
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FPF Develops Checklist & Guide to Help Schools Vet AI Tools for Legal Compliance
FPF’s Youth and Education team has developed a checklist and accompanying policy brief to help schools vet generative AI tools for compliance with student privacy laws. Vetting Generative AI Tools for Use in Schools is a crucial resource as the use of generative AI tools continues to increase in educational settings. It’s critical for school […]
FPF Submits Feedback and Comments on UNICEF’s Draft Policy Guidance on AI for Children
Last week, FPF submitted feedback and comments to the United Nations Children’s Fund (UNICEF) on the Draft Policy Guidance on Artificial Intelligence (AI) for Children, which seeks “to promote children’s rights in government and private sector AI policies and practices, and to raise awareness of how AI systems can uphold or undermine children’s rights.” The […]
FPF Letter to NY State Legislature
On Friday, June 14, FPF submitted a letter to the New York State Assembly and Senate supporting a well-crafted moratorium on facial recognition systems for security uses in public schools.