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Paper Number
2220
Paper Type
Short
Description
Artificial intelligence (AI) has the potential to dramatically change the way decisions are made and organizations are managed. As of today, AI is mostly applied as a collaboration partner for humans, amongst others through delegation of tasks. However, it remains to be explored how AI should be optimally designed to enable effective human-AI collaboration through delegation. We analyze influences on human delegation behavior towards AI by studying whether increasing users' knowledge of AI's error boundaries leads to improved delegation behavior and trust in AI. Specifically, we analyze the effect of showing AI's certainty score and outcome feedback alone and in combination using a 2x2 between-subject experiment with 560 subjects. We find that providing both pieces of information can have a positive effect on collaborative performance, delegation behavior, and users' trust in AI. Our findings contribute to the design of AI for collaborative settings and motivate research on factors promoting delegation to AI.
Recommended Citation
Taudien, Anna; Fuegener, Andreas; Gupta, Alok; and Ketter, Wolfgang, "Calibrating Users’ Mental Models for Delegation to AI" (2022). ICIS 2022 Proceedings. 16.
https://aisel.aisnet.org/icis2022/user_behaivor/user_behaivor/16
Calibrating Users’ Mental Models for Delegation to AI
Artificial intelligence (AI) has the potential to dramatically change the way decisions are made and organizations are managed. As of today, AI is mostly applied as a collaboration partner for humans, amongst others through delegation of tasks. However, it remains to be explored how AI should be optimally designed to enable effective human-AI collaboration through delegation. We analyze influences on human delegation behavior towards AI by studying whether increasing users' knowledge of AI's error boundaries leads to improved delegation behavior and trust in AI. Specifically, we analyze the effect of showing AI's certainty score and outcome feedback alone and in combination using a 2x2 between-subject experiment with 560 subjects. We find that providing both pieces of information can have a positive effect on collaborative performance, delegation behavior, and users' trust in AI. Our findings contribute to the design of AI for collaborative settings and motivate research on factors promoting delegation to AI.
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