AI in health care
In the first half of 2025, 34 states introduced over 250 AI-related health bills. The bills generally fall into four categories: disclosure requirements, consumer protection, insurers’ use of AI, and clinicians’ use of AI.
Bills about transparency define requirements for information that AI system developers and organizations that deploy the systems disclose.
Consumer protection bills aim to keep AI systems from unfairly discriminating against some people and ensure that users of the systems have a way to contest decisions made using the technology.
Bills covering insurers provide oversight of the payers’ use of AI to make decisions about health care approvals and payments. And bills about clinical uses of AI regulate use of the technology in diagnosing and treating patients.
Facial recognition and surveillance
In the US, a long-standing legal doctrine that applies to privacy protection issues, including facial surveillance, is to protect individual autonomy against interference from the government. In this context, facial recognition technologies pose significant privacy challenges as well as risks from potential biases.
Facial recognition software, commonly used in predictive policing and national security, has exhibited biases against people of color and consequently is often considered a threat to civil liberties. A pathbreaking study by computer scientists Joy Buolamwini and Timnit Gebru found that facial recognition software poses significant challenges for Black people and other historically disadvantaged minorities. Facial recognition software was less likely to correctly identify darker faces.
Bias also creeps into the data used to train these algorithms, for example when the composition of teams that guide the development of such facial recognition software lack diversity.
By the end of 2024, 15 states in the US had enacted laws to limit the potential harms from facial recognition. Some elements of state-level regulations are requirements on vendors to publish bias test reports and data management practices, as well as the need for human review in the use of these technologies.
