AI Policy Wiki
Dashboard

Tom Mitchell

medium confidence · updated 2026-08-17

Carnegie Mellon machine-learning researcher, author of the field's standard early textbook and first chair of CMU's Machine Learning Department. Co-author with Eric Horvitz of a historical treatment of scientific progress in AI, and co-chair of the National Academies study on AI and the future of work.

Tom M. Mitchell (born August 9, 1951) is an American computer scientist at Carnegie Mellon University, where he holds the title of Founders University Professor. He wrote Machine Learning (McGraw Hill, 1997), described as one of the first textbooks in the field, and in 2006 became the first chair of CMU's Machine Learning Department, the first academic department of its kind (Source: en.wikipedia.org). His policy-relevant work has centred on AI and labour.

Background and academic career

Mitchell received a B.S. in electrical engineering from MIT in 1973 and a Ph.D. from Stanford University in 1979 under Bruce G. Buchanan. He joined Carnegie Mellon as a professor in 1986 and was named E. Fredkin Professor in the School of Computer Science in 1999. He became the founding chair of the Machine Learning Department in 2006, was named University Professor in 2009, and served as interim dean of the School of Computer Science from 2018 to 2019 (Source: en.wikipedia.org).

Alongside the 1997 textbook, he co-edited several volumes on machine-learning research between 1983 and 1996 (Source: en.wikipedia.org).

Research

Two lines of Mitchell's research are noted on the public record. The Never-Ending Language Learning (NELL) project ran a system that continuously extracted structured knowledge from web text and improved its own extraction over time. Separately, he worked on brain-imaging and neural decoding, reported as "mind-reading" computer research, which used functional imaging to identify what a subject was thinking about (Source: en.wikipedia.org).

Work on AI and labour

Mitchell co-chaired the National Academies study on AI and the future of work, which took the position that the near-term effect of AI on employment runs through task recomposition within occupations rather than through wholesale occupational displacement — the framing that later task-level empirical work operationalized. See AI Labor Disruption, AI and Productivity.

With Eric Horvitz he co-authored a treatment of scientific progress in AI supplying historical context to current capability measurement (Scientific Progress in Artificial Intelligence: History, Status, and Futures — Eric Horvitz and Tom M. Mitchell (2024)). See History of AI.

Honours and appointments

YearRecognition
1984NSF Presidential Young Investigator Award
1990Fellow, Association for the Advancement of Artificial Intelligence
2008Fellow, American Association for the Advancement of Science
2010Member, National Academy of Engineering
2016Fellow, American Academy of Arts and Sciences

He is a past president of the Association for the Advancement of Artificial Intelligence, and sits on the scientific advisory board of the Allen Institute for AI and the science board of the Santa Fe Institute (Source: en.wikipedia.org).

Relationships

Sources