Introduction to the Special Issue on the Human-Algorithm Connection.
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| Authors: | Caro, Felipe1 (AUTHOR) felipe.caro@anderson.ucla.edu, Colliard, Jean-Edouard2 (AUTHOR) colliard@hec.fr, Katok, Elena3 (AUTHOR) ekatok@utdallas.edu, Ockenfels, Axel4 (AUTHOR) ockenfels@uni-koeln.de, Stier-Moses, Nicolas5 (AUTHOR) nicostier@yahoo.com, Tucker, Catherine6 (AUTHOR) cetucker@mit.edu, Wu, D. J.7 (AUTHOR) dj.wu@scheller.gatech.edu |
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| Source: | Management Science (INFORMS). Jan2026, Vol. 72 Issue 1, p1-13. 13p. |
| Subject Terms: | *Algorithms, *Decision making, *Human-computer interaction, *Algorithmic bias, *Artificial intelligence, Interdisciplinary research |
| Abstract: | The article introduces a special issue of Management Science dedicated to the "Human-Algorithm Connection," emphasizing the growing importance of understanding the interplay between humans and algorithms in decision-making processes. It features 39 interdisciplinary papers selected from 319 submissions, categorized into themes such as augmentation of human decision-making, algorithm aversion/adoption, algorithmic fairness, and the future of work. The research highlights various methodologies, including theoretical, empirical, and experimental approaches, and addresses critical issues like the enhancement of human capabilities through algorithms, the challenges of algorithm aversion, and the implications of algorithmic fairness in societal contexts. The findings underscore the necessity for organizations to adapt to the increasing integration of algorithms and AI in their operations. [Extracted from the article] |
| Database: | Entrepreneurial Studies Source |
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| Abstract: | The article introduces a special issue of Management Science dedicated to the "Human-Algorithm Connection," emphasizing the growing importance of understanding the interplay between humans and algorithms in decision-making processes. It features 39 interdisciplinary papers selected from 319 submissions, categorized into themes such as augmentation of human decision-making, algorithm aversion/adoption, algorithmic fairness, and the future of work. The research highlights various methodologies, including theoretical, empirical, and experimental approaches, and addresses critical issues like the enhancement of human capabilities through algorithms, the challenges of algorithm aversion, and the implications of algorithmic fairness in societal contexts. The findings underscore the necessity for organizations to adapt to the increasing integration of algorithms and AI in their operations. [Extracted from the article] |
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| ISSN: | 00251909 |
| DOI: | 10.1287/mnsc.2023.intro.v72.n1 |
