Biography
Dr. Daniel B. Shank is an associate professor of psychological science at Missouri University of Science & Technology specializing in the areas of social psychology and technology. His research primarily focuses on social psychological interactions with and perceptions of artificial intelligence, including morality, emotions, relationships, impressions, and behavior toward AIs.
He has received over $3 million in external and internal grants including major grants from the National Science Foundation and the Army Research Office.
His book, The Machine Penalty: The Consequences of Seeing Artificial Intelligence as Less Than Human, published by Palgrave-Macmillan came out in 2025.
He has published over 50 peer-reviewed articles in psychology, sociology, and behavioral science technology journals such as the Computers in Human Behavior; Journal of Personality and Social Psychology; Journal of Experimental Psychology Applied; Technology, Mind, and Behavior; Social Science Research; American Behavioral Scientist; and Human Factors
Dr. Shank previously was a postdoctoral research fellow at the University of Alabama Birmingham and at the University of Melbourne in Australia.
Education
- PhD, Sociology, University of Georgia
- MA, Sociology, University of Georgia
- MS, Artificial Intelligence, University of Georgia
- BA, Computer Science, Harding University
Awards
- 2024. Faculty Excellence Award. Missouri S&T.
- 2023. Experiential Learning Award. Missouri S&T.
- 2023. Innovative Teaching Award. College of Arts, Science, and Education, Missouri S&T.
- 2021 Research Award Given for involving students in research.
College of Arts, Science, and Business, Missouri S&T. - 2019 Faculty Research Award Missouri S&T
Professional links
2025 Book
The Machine Penalty: The Consequences of Seeing Artificial Intelligence as Less Than Human
Abstract: This book makes the argument that comparing AI to humans leads us to diminish similar outcomes from AI across situations. This may be taking a human’s advice for a restaurant recommendation over an AI’s or believing that AI can’t be as biased as people can when denying loans to others. This machine penalty is caused both by comparing humans and AI in terms of appearance, identity, behavior, mind, and essence, and by situations involving controllable, personal, important, subjective, or moral decisions. It can be applied across many different situations, where we diminish different AI outcomes. We penalize machines’ influence when they give advice, fairness when they evaluate people, blame when they cause harm, value when they produce art, and satisfaction when they provide companionship. The result is immediate consequences in those domains and downstream consequences for society. This monograph brings together diverse research from human-computer interaction, psychology, sociology, and communication including theories such as Computers Are Social Actors, anthropomorphism, mind perception, and algorithm aversion to present an expansive argument and evidence for the machine penalty.
Publisher: https://link.springer.com/book/10.1007/978-3-031-86061-4
Amazon: https://www.amazon.com/Machine-Penalty-Consequences-Artificial-Intelligence/dp/3031860608