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Contribution Details
| Type | Journal Article |
| Scope | Discipline-based scholarship |
| Title | Rise of the machines: Delegating decisions to autonomous AI |
| Organization Unit | |
| Authors |
|
| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published in final form |
| Language |
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| Journal Title | Computers in Human Behavior |
| Publisher | Elsevier |
| Geographical Reach | international |
| ISSN | 0747-5632 |
| Volume | 134 |
| Page Range | 107308 |
| Date | 2022 |
| Abstract Text | Delegation is an important part of organizational success and can be used to overcome personal shortcomings and draw upon the expertise and abilities of others. However, delegation comes with risks and uncertainties, as it entails a transfer of power and loss of control. Indeed, research has documented that people tend to under-delegate to other humans, often leading to poor decisions and ultimately negative economic consequences. Today, however, people are faced with a new delegation choice: Artificial Intelligence (AI). Fueled by Big Data, AI is rapidly becoming more intelligent and frequently outperforming human forecasters and decision-makers. Given this evolution of computational autonomy, researchers need to revisit the hows and whys of decision delegation and clarify not only whether people are willing to cede control to AI agents but also whether AI can reduce the under-delegation that is especially pronounced when people are faced with decisions that spur a high desire for control. By linking research on decision delegation, social risk, and control premium to the emerging field of trust in AI, we propose and find that people prefer to delegate decisions to AI as compared to human agents, especially when decisions entail losses (Studies 1–3). Results further illuminate the underlying psychological process involved (Study 1 and 2) and show that process transparency increases delegation to humans but not to AI (Study 3). These findings have important implications for research on trust in AI and the applicability of autonomous AI systems for managers and decision makers. |
| Free access at | DOI |
| Digital Object Identifier | 10.1016/j.chb.2022.107308 |
| Other Identification Number | merlin-id:22428 |
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| Additional Information | Lizenz: CC BY-NC-ND 4.0 |