Employees more likely to second-guess interpretable algorithms
New research shows that people are more likely to trust complicated machine learning models over models that they’re able to understand and troubleshoot.
Faculty
Kate Kellogg is the David J. McGrath jr Professor of Management and Innovation, a Professor of Business Administration at the MIT Sloan School of Management, and Group Head for the Work and Organization Studies Department.
Kate's research focuses on helping organizations implement new technologies, on-the-ground in everyday work, to improve decision making, collaboration among diverse experts, and learning in a time of rapid digital transformation. She shows how organizations can gain user acceptance of new technologies by including users in the technology design process, providing training to give employees the skills they need to work with new technologies, and designing new technologies with employees in mind.
Kate's current projects examine the collaborative development and implementation of AI-based technologies for frontline providers in healthcare organizations.
She has authored dozens of articles that have appeared in top journals across the fields of management, organization studies, healthcare, sociology, work and employment, and information systems research. Her research has won awards from the Academy of Management, the American Sociological Association, the Alfred P. Sloan Foundation, the Institute for Operations Research and the Management Sciences, and the National Science Foundation.
Over the past decade, Kate has partnered with for-profit and not-for-profit organizations to help improve collaboration among diverse experts, use technologies to improve internal knowledge sharing, and manage the human aspects of new technology implementation in order to thrive in fast-paced and uncertain contexts.
Before coming to MIT Sloan, Kate worked as a management consultant for Bain & Company and for Health Advances. She received her PhD in organization studies from MIT, her MBA from Harvard University, and her BA from Dartmouth College in biology and psychology.
Kellogg, Katherine C. and Shiri Sadeh-Sharvit. Frontiers in Psychiatry Vol. 13, (2022).
Kellogg, Katherine C., Mark Sendak, and Suresh Balu. MIT Sloan Management Review, May 4, 2022.
DeStefano, Timothy, Katherine C. Kellogg, Michael Menietti, and Luca Vendraminelli, MIT Sloan Working Paper 6797-22. Cambridge, MA: MIT Sloan School of Management, October 2022. WSJ Article.
Singer, Sara J., Katherine C. Kellogg, Ari B. Galper, and Deborah Viola. Healthcare Management Review Vol. 47, No. 2 (2022): E21-E31.
Kellogg, Kate. Wall Street Journal, November 28, 2021.
Kellogg, Katherine C. MIT Sloan Management Review, September 2021.
New research shows that people are more likely to trust complicated machine learning models over models that they’re able to understand and troubleshoot.
An AI productivity boom is coming. Here’s what managers need to know to roll out intelligent technology that’s ethical and worker-centric.
"Managers face special challenges leading teams whose members mix time in the office with remote work."
"Getting workers to actually use the technologies will turn out to be just as important as making sure the systems work in the first place."
“Where I see these technologies really being able to help with access is through training and providing more targeted supervisory support.”
"Documentation is just a crushing burden. We believe that these AI technologies have tremendous potential to change clinicians' quality of life."