Shank The Machine Penalty

The Consequences of Seeing Artificial Intelligence as Less Than Human
1. Auflage 2025
ISBN: 978-3-031-86061-4
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 288 Seiten

Reihe: Progress in Mathematics

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.

Zielgruppe

Research

Autoren/Hrsg.

Shank, Daniel B.

Inhaltsverzeichnis

1. Introduction.- Part I The Penalty Explained.- 2. What is the Machine Penalty?.- 3. What is the Machine Penalty?.- 4. Human-AI Comparisons.- 5. How Do We Diminish Machines?.- 6. The Machine Penalty Causes.- Part II The Penalty Applied.- 7. Giving Advice.- 8. Evaluating People.- 9. Causing Harm.- 10. Producing Art.- 11. Providing Companionship.- Part III The Penalty Resolved.- 12. Downstream Consequences.- 13. Responding to the Machine Penalty.- 14. The Machine Penalty in the Future.

Über Autor(innen)

Dr. Daniel B. Shank is an Associate Professor of Psychological Science at Missouri University of Science and Technology, USA, specializing in the areas of social psychology and technology, especially artificial intelligence. He has received over $3 million in external and internal grants, including major external grants from the and the . He has published over 50 peer-reviewed articles, including in and . He’s received awards for Research, Teaching, Student Impact, Experiential Learning, and overall Excellence from Missouri S&T.

Dr. Shank previously received a BA in Computer Science from Harding University, an MS in Artificial Intelligence and an MA and PhD in Sociology from the University of Georgia. Before joining Missouri S&T, he was a postdoctoral research fellow at the University of Alabama Birmingham and at the University of Melbourne in Australia.