Matt Groh is a Donald P. Jacobs Scholar and Assistant Professor of Management and Organizations. His research examines the dynamics of human-AI collaboration in deepfake detection, medical diagnosis, and empathic communication.
Professor Groh's research has been published in Proceedings on National Academy of Science (PNAS), Nature Medicine, Science, Computer Supported Collaborative Work (CSCW), Affective Computing and Intelligence Interactions (ACII), and Communications of the ACM among other journals and conferences. His work has been featured in the popular press including The New York Times, Wall Street Journal, Science, Scientific American, NPR, Le Monde, Aeon, and Fast Company.
Professor Groh received his BA from Middlebury College with a major in economics and minors in mathematics and Arabic and received his MA and PhD in Media Arts and Sciences from MIT.
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Human-AI Collaboration, Computational Social Science, Applied Machine Learning, Affective Computing, Generative AI
Artificial intelligence (AI) is a transformative technology and a modern day equivalent to fire in the early stages of human civilization. It is a tool that can be used to solve complex problems, make predictions, automate tasks, and enhance productivity. But like fire, it has a dual nature and has potential for both good and bad outcomes. This course requires no prior technical knowledge and is designed for people who want to lead and manage the deployment of AI systems in the real-world whether at a startup or a large organization. The goal of the course is to build intuition and understanding of what AI is, how machine learning works, where these tools tend to succeed and fail, and how to navigate the ethical implications of these tools. We will explore a wide range of business applications, examine tools ranging from ChatGPT and Midjourney to DeepBlue and AlphaGo to the Twitter and TikTok recommendation systems to many more, and discuss best practices for managing teams of humans assisted by these tools. This course is a lecture-based course with case-based discussions, individual assignments, a midterm, and a final group project. By the end, you should be an expert at identifying promising use-cases, understanding current limitations, and recognizing potential pitfalls of AI such that you are capable of applying human and machine thought partnerships to grow new businesses and disrupt Grand Masters in any field.
