Paper Number
2164
Paper Type
Complete Research Paper
Abstract
Given the multitude of entities involved in the production and implementation of AI service and human-AI joint service, determining the responsibility attribution among all relevant entities is challenging. To address this issue, we conduct a systematic review of recent studies from the last 5 years (2019-2023) focusing on responsibility attribution in both AI service and human-AI joint service. We find that researchers have yet to agree on two normative questions: (1) whether AI agents should be considered as independent responsibility holders and (2) who should be included as responsibility holders. Examining empirical studies, we synthesize and map out both the antecedents and outcomes of perceived responsibility attribution. Especially, we note that empirical researchers adopt various empirical designs and present mixed findings. Based on these findings, we propose potential future directions for research to address responsibility attribution for AI service and human-AI joint service.
Recommended Citation
Huang, Xin and Suang, Heng Cheng, "A Systematic Review on Responsibility Attribution in AI Service and Human-AI Joint Service" (2024). ECIS 2024 Proceedings. 11.
https://aisel.aisnet.org/ecis2024/track15_social_ict/track15_social_ict/11
A Systematic Review on Responsibility Attribution in AI Service and Human-AI Joint Service
Given the multitude of entities involved in the production and implementation of AI service and human-AI joint service, determining the responsibility attribution among all relevant entities is challenging. To address this issue, we conduct a systematic review of recent studies from the last 5 years (2019-2023) focusing on responsibility attribution in both AI service and human-AI joint service. We find that researchers have yet to agree on two normative questions: (1) whether AI agents should be considered as independent responsibility holders and (2) who should be included as responsibility holders. Examining empirical studies, we synthesize and map out both the antecedents and outcomes of perceived responsibility attribution. Especially, we note that empirical researchers adopt various empirical designs and present mixed findings. Based on these findings, we propose potential future directions for research to address responsibility attribution for AI service and human-AI joint service.
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