Abstract
Artificial intelligence (AI) promises businesses superior decisions that outperform those of domain experts. However, AI systems may fail on the ground when they are not developed in collaboration with the experts they seek to bypass. This raises the question of how to manage the collaborative development of AI. Building on a comparative field study, we reveal three key challenges of collaborative AI development in the area of consulting, hiring, and radiology. Based on these findings, we derive guidelines for managers that help them to facilitate the close engagement between AI developers and experts.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 56th Annual Hawaii International Conference on System Sciences, HICSS 2023 |
| Editors | Tung X. Bui |
| Publisher | HICSS Conference Office Department of IT Management, Shidler College of Business, University of Hawaii |
| Pages | 6139-6148 |
| Number of pages | 10 |
| ISBN (Electronic) | 9780998133164 |
| Publication status | Published - 2023 |
| Event | 56th Annual Hawaii International Conference on System Sciences, HICSS 2023 - Virtual, Online, United States Duration: 3 Jan 2023 → 6 Jan 2023 |
Publication series
| Name | Proceedings of the Annual Hawaii International Conference on System Sciences |
|---|---|
| Volume | 2023-January |
| ISSN (Print) | 1530-1605 |
Conference
| Conference | 56th Annual Hawaii International Conference on System Sciences, HICSS 2023 |
|---|---|
| Country/Territory | United States |
| City | Virtual, Online |
| Period | 3/01/23 → 6/01/23 |
Bibliographical note
Publisher Copyright:© 2023 IEEE Computer Society. All rights reserved.
Keywords
- AI development
- collaboration
- comparative field study
- developers
- domain experts