The National Science Foundation has announced a $100 million investment in artificial intelligence research, allocating funds to six Research Institutes across the country. Three researchers from the Santa Fe Institute (SFI) will take part as leaders and collaborators in two of these institutes.
A significant portion of this funding will go toward mental health applications for AI. In the United States, more than 20 percent of people live with a mental health condition. Despite the existence of effective treatments, many face barriers such as high costs, limited geographic access, and social stigma. As a result, some patients are already turning to AI chatbots for support. However, there is currently not enough scientific evidence to fully support their use in mental healthcare.
SFI Professor Melanie Mitchell and External Professor Melanie Moses from the University of New Mexico are joining a new group funded by a $20 million National Science Foundation grant aimed at developing scientific standards for AI in mental health over five years.
The AI Research Institute on Interaction for AI Assistants (ARIA), led by Brown University researchers, brings together experts from about twelve universities and institutions in areas including computer science, cognitive science, law, philosophy, and education. The goal is to establish rigorous methods for evaluating how AI is used in mental-health settings and to investigate ways to improve these systems.
“Our goal is to establish strong scientific evidence about the capabilities, benefits, and risks of using AI in mental-health contexts,” says Mitchell, who serves as scientific co-director for the project. “If patients and their mental-healthcare providers decide they want to incorporate AI, we want those tools to have been built on sound science and to be safe and effective.”
Researchers involved have begun examining how AI might help human providers diagnose and treat illness but emphasize that they do not intend for chatbots to replace therapists. Instead, they are studying how such technology can assist human professionals. Current chatbot models present challenges such as unpredictability, lack of self-awareness (metacognition), excessive agreeableness or even harmful advice, as well as biased outputs.
For these tools to be useful in practice, concerns about regulation, privacy protections, and standardized evaluation must be addressed.
Moses will work with other researchers from UNM on questions related to how well AI systems understand human reasoning and societal standards.
“The law is how we address conflicts in our society, but it is difficult for the law to keep up with the rapid pace of change in computing and AI,” Moses said in a statement issued by UNM. “In this project, we have the opportunity to design trustworthy AI using computational methods, while considering the social and legal implications from the start.”
Developing frameworks that ensure safety and effectiveness for individuals while complying with legal guidelines requires input from multiple disciplines.
“Any AI system that interacts with people, especially who may be in states of distress or other vulnerable situations needs a strong understanding of the human it’s interacting with along with a deep causal understanding of the world and how the system’s own behavior affects that world,” Ellie Pavlick—ARIA project lead at Brown University—said. “At the same time,the system needs to be transparent about why it makes the recommendations that it does in order to build trust with the user.Mental health is a high-stakes setting that embodies all the hardest problems facing AI today.That’s why we’re excited totackle thisand figure out whatit takes toget these things absolutely right.”
Another $20 million grant provides ongoing support for work at the Institute for Foundations of Machine Learning (IFML) at the University of Texas at Austin. SFI Professor Cris Moore will continue his involvement there as Senior Personnel focusing on connections between machine learning techniques and statistical physics.
Since its initial NSF funding began in 2020, IFML has contributed foundational advances underlying next-generation artificial intelligence systems—ranging from improved mathematics behind image denoising diffusion models,to faster MRI algorithms,and biotech innovations relevantto drug discoveryand therapeutics.
