Artificial intelligence is no longer only a technical question. At the recent AI and Society Forum at MIT, experts from across the Institute examined how AI could influence labor, the nature of work, civil discourse, election administration, and creative practice.
The event combined research presentations, panel discussions, and a musical performance exploring the use of generative artificial intelligence in the arts. Its broader message was clear: understanding AI's societal impact requires more than engineering alone.
Why MIT framed AI as a society-wide issue
The forum was co-organized by the School of Humanities, Arts, and Social Sciences (SHASS) and the Social and Ethical Responsibilities of Computing (SERC). It was presented in collaboration with the MIT Generative AI Impact Consortium (MGAIC) and the MIT Human Insight Collaborative (MITHIC).
Agustin Rayo, the Kenan Sahin Dean of SHASS, and Dan Huttenlocher, dean of the MIT Schwarzman College of Computing, opened the event by emphasizing the need for research that crosses disciplinary lines. Rayo said the focus on social consequences was directly connected to MIT's technical mission.
“Paying attention to the societal consequences of AI is not a departure from MIT’s mission; it’s a way of ensuring that our technical leadership has maximum impact,” Rayo said.
Huttenlocher made a related point about the speed of change in computing and AI. As these systems grow more capable and more widely used, he argued, institutions need better ways to understand both their strengths and their limits.
“Understanding where AI excels and where it falls short is essential not only to unlocking its benefits, but also to avoiding critical errors, overreliance, and unintended consequences,” Huttenlocher said.
Work may change through expertise, not just automation
The May 12 forum was held in the Tull Concert Hall in MIT's Linde Music Building. It opened with a keynote presentation from economist David Autor, the Daniel (1972) and Gail Rubinfeld Professor in the MIT Department of Economics.
Autor challenged the simple idea that AI's main effect will be to eliminate jobs. His argument focused instead on how technology changes the scarcity and value of human expertise. In that framing, the key issue is not automation in the abstract, but what kind of work is being automated.
If technology removes routine support tasks, it may change work in one way. If it replaces expert tasks, the consequences may be different. Autor argued that AI will likely create new specialized work, and that societies should think ahead about worker training, wage insurance, and broader capital ownership.
A panel then explored how work is changing and what that means for society. Daniela Rus, the MIT Panasonic Professor of Computer Science and director of the Computer Science and Artificial Intelligence Laboratory (CSAIL), described the potential for AI and robotics to support people at work.
“I’d like to imagine the robot as your friend and assistant, as someone who watches you and figures out how to help you as someone you can task at a high level,” she said.
But Rus also stressed that human judgment remains central. AI tools may help with tasks, but the person still has to decide what happens next. That distinction matters because it separates assistance from responsibility.
The future of work depends on what gets created next
David Mindell, professor of Aeronautics and Astronautics and the Dibner Professor of the History of Engineering and Manufacturing in the Program in Science, Technology, and Society, placed AI in a longer history of changing work. He said the nature of work has always shifted, but the important question is what new work emerges.
That point reframes the AI debate around preparation. If new kinds of work are expected, then education, tools, professions, and economic structures all become part of the response.
“We need to be supporting individuals, the economy, professions, to constantly be creating the new work,” he said.
The panel also discussed the balance between efficiency and safety. Mindell pointed to cargo flights that require six pilots because of the length of the flight. The example showed that some systems are not just bundles of tasks to optimize; they are also safety structures built over time.
Sendhil Mullainathan, the Peter de Florez Professor with dual appointments in the MIT departments of Economics and Electrical Engineering and Computer Science (EECS), described AI as offering productivity improvements. He also warned that productivity gains should not automatically be treated as the same thing as long-term growth.
For Mullainathan, one clear feature of the moment is uncertainty. He said organizations are likely to restructure, even if the exact form of that restructuring is not yet knowable.
Democracy raises a different set of AI risks
The day's second session turned to AI and democracy. Chara Podimata, the Class of 1942 Career Development Assistant Professor and assistant professor of operations research and statistics in the MIT Sloan School of Management, presented research on auditing large language models for bias in election information.
Her work examined how chatbots respond when different people ask for election-related information. A longitudinal study of 12 major models during the 2024 U.S. presidential election season found that responses varied dramatically based on stated demographics and political leanings.
Podimata's research team is now working on a new audit of the 2026 U.S. midterm elections. That effort uses a redesigned survey with input from political science experts.
In the panel discussion, experts considered both possible harms and possible benefits. Bailey Flanigan, the Theodore T. Miller (1922) Career Development Professor in the Department of Political Science, who holds an MIT Schwarzman College of Computing shared position with EECS, questioned the idea that AI should be used simply to make people reach decisions or consensus faster.
Her concern was procedural. Democratic decision-making is not only about speed or output; it also depends on the rituals and processes through which people come together and make decisions.
Charles Stewart III, the Kenan Sahin (1963) Distinguished Professor of Political Science and founding director of the MIT Election Data and Science Lab, pointed to another challenge: government structures do not change as quickly as technology. Stewart said his biggest concern is the potential for AI to lead to chaos during and after elections.
Human judgment remains the common thread
Across the forum, the topic shifted from labor to democracy to the arts, but a common pattern emerged. AI can create efficiencies, generate new forms of work, and support new creative or organizational possibilities. At the same time, it can also introduce overreliance, bias, procedural loss, and uncertainty.
The forum's strongest conclusion was not that AI is simply beneficial or dangerous. It was that the consequences depend on how people, institutions, and professions choose to use it. Technical capacity matters, but so do judgment, governance, training, safety standards, and the social settings in which AI systems operate.