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Accelerated research about generative AI from MIT Sloan


In response to the rapid rise of generative artificial intelligence, in the fall of 2023 MIT president Sally Kornbluth and provost Cynthia Barnhart issued a call for research proposals related to how the technology will transform people’s lives and work.

The result is a new open-access collection of 25 research papers that provide road maps, policy recommendations, and calls for action about generative AI from MIT experts. Topics range from using generative AI in education, manufacturing, and drug development to how generative AI will affect inequality and music discovery.

The research papers are being widely shared as quickly as possible because generative AI is evolving at a rapid pace and they could “serve as a springboard for further research, study and conversation about how we as a society can build a successful AI future,” Kornbluth said in an introduction to the collection. The papers are all works in progress and may be further developed. They have not been formally peer-reviewed.

MIT Sloan faculty members and researchers contributed to the following research projects. 

AI in engineering and manufacturing 

From Automation to Augmentation: Redefining Engineering Design and Manufacturing in the Age of NextGen-AI

This paper examines the barriers, risks, and potential rewards of using generative AI for design and manufacturing. MIT Institute professor  MIT Sloan economist  and co-authors interviewed manufacturing experts and industry leaders to find weaknesses that need to be addressed by the next generation of generative AI tools.

Implementing Generative AI in U.S. Hospital Systems

AI can transform health care, but there are often challenges when new technologies are introduced into clinical settings. In this paper, the researchers, including MIT Sloan professors  and  look at the difference between deploying narrow, or traditional, AI and generative AI in health care systems and how challenges associated with both technologies inform where AI might be most effective. 

AI’s impact on work and productivity 

The Impact of Generative AI on Labor Market Matching

MIT Sloan PhD student Justin Kaashoek and MIT Sloan professors  and discuss areas where generative AI might appear in the labor market, including AI-generated cover letters, resumes, and job postings. The researchers examine the risks and benefits of these uses and identify ways to mitigate risks while promoting the benefits of AI.

The Productivity Effects of Generative AI: Evidence From a Field Experiment With GitHub Copilot

Two field experiments found evidence that software developers at Microsoft and Accenture who were given access to an AI-based coding assistant became more productive. The researchers, including MIT Sloan professor  indicated that their findings are preliminary and that the team is still in the process of collecting additional data.

Bringing Worker Voice Into Generative AI

Input from workers can increase the likelihood that organizations use generative AI tools effectively and that workers’ job quality improves. The researchers, including MIT Sloan professors  and  and MIT Sloan MBA candidate Ben Likis, identified ways to bring workers’ voices into the development and use of generative AI.

Practical AI applications 

Generative AI From Theory to Practice: A Case Study of Financial Advice

Researchers, including MIT Sloan professor  looked at the most pressing issues facing the adoption of large language models. Using financial advice as a test for determining the shortcomings of LLMs, the researchers suggest ways to improve generative AI in general. 

Labeling AI-Generated Content: Promises, Perils, and Future Directions

How should policymakers, platforms, and practitioners decide how to label AI-generated content? MIT Sloan professor  and other researchers outlined two goals for labels: communicating whether a piece of content was created or edited using AI, and diminishing the likelihood that content misleads or deceives its viewers. They found that under certain conditions, labeling can decrease individuals’ likelihood of believing or engaging with misleading AI-generated images.

Social implications of AI

Data Authenticity, Consent, and Provenance for AI Are All Broken: What Will It Take to Fix Them?

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Generative AI and the Future of Inequality

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See all the research: An MIT Exploration of Generative AI

For more info Sara Brown Senior News Editor and Writer