MIT’s Daron Acemoglu, David Autor, and Simon Johnson make the case for federal policies to encourage the development of AI that complements workers.
When professionals try to validate outputs, generative AI often responds not with corrections or candor but with escalating persuasion tactics.
Organizations have options when it comes to using or adapting off-the-shelf large language models to handle tasks or business use cases.
Adela Jamal, SFMBA ’22, and Sebastian Barriga, SFMBA ’22, talk about their journey from diverse global careers to launching an applied AI-focused venture fund.
Charles E. and Susan T. Harris Professor, Professor, Finance, Director, Laboratory for Financial Engineering
Impact: AI-driven, Personalized Transparent Decision-support Tool Enables Improved Treatment Decisions
New research shows that people are more likely to trust complicated machine learning models over models that they’re able to understand and troubleshoot.
Organizations are struggling to succeed with AI. Research from the MIT Center for Information Systems Research shows common mistakes and how to overcome them.
Behavioral science can help identify who is open to AI-enhanced insights, helping companies hone their customer experience strategy.
Here’s what businesses need to know as AI agents move from centralized systems toward a decentralized network of trillions of personal and organizational agents.
