Conference Paper/Proceeding/Abstract 358 views
To err is AI
,
Alan Dix,
Emanuele Panizzi ,
Stefano Zeppieri
CHItaly '23: Proceedings of the 15th Biannual Conference of the Italian SIGCHI Chapter, Pages: 1 - 11
Swansea University Author: Alan Dix
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DOI (Published version): 10.1145/3605390.3605414
Abstract
In this work, we analyze the different contexts in which one chooses to integrate artificial intelligence into an interface and the implications of this choice in managing user interaction. While AI in systems can provide significant benefits, it is not infallible and can make errors that seriously...
| Published in: | CHItaly '23: Proceedings of the 15th Biannual Conference of the Italian SIGCHI Chapter |
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| ISBN: | 979-8-4007-0806-0 |
| Published: |
New York, NY, USA
ACM
2023
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| Online Access: |
http://dx.doi.org/10.1145/3605390.3605414 |
| URI: | https://cronfa.swan.ac.uk/Record/cronfa64783 |
| Abstract: |
In this work, we analyze the different contexts in which one chooses to integrate artificial intelligence into an interface and the implications of this choice in managing user interaction. While AI in systems can provide significant benefits, it is not infallible and can make errors that seriously affect users. We aim to understand how to design more robust human-AI systems so that these initial AI errors do not lead to more catastrophic failures. To prevent failures, it is essential to detect errors as early as possible and have clear mechanisms to repair them. However, detecting errors in AI systems can be challenging. Therefore, we examine various approaches to error detection and repair, including post-hoc estimation, the use of traces and ambiguity, and multiple sensor layers. |
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| Keywords: |
HCI, AI, errors, failures, error detection, error repair, user perception, interaction design |
| College: |
Faculty of Science and Engineering |
| Start Page: |
1 |
| End Page: |
11 |