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.
Kate's research focuses on helping knowledge workers and organizations develop and implement Predictive and Generative AI products, on-the-ground in everyday work, to improve decision making, collaboration, and learning. She shows how organizations can gain user acceptance and effective use of intelligent products and services by including users in the technology design process, providing training to give employees the skills they need to work with intelligent technologies, and designing the technologies with employees in mind.
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, and her BA from Dartmouth in biology and psychology.
Featured Publication
"AI on the Front Lines."Kellogg, Katherine C., Mark Sendak, and Suresh Balu. MIT Sloan Management Review, May 4, 2022.
Featured Publication
"Pragmatic AI-augmentation in Mental Healthcare: Key Technologies, Potential Benefits, and Real-world Challenges and Solutions for Frontline Clinicians."Kellogg, Katherine C. and Shiri Sadeh-Sharvit. Frontiers in Psychiatry Vol. 13, (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, Katherine C. MIT Sloan Management Review, September 2021.
Kellogg, Katherine C. Organization Science (2021): 1-30.
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.
"We foresee new AI tools making it easier for managers to provide high-quality coaching more efficiently."
"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.”