Paper Type
Short
Paper Number
PACIS2025-1499
Description
This study investigates the implementation process of artificial intelligence (AI) accountability through an in-depth case study of a Chinese commercial bank. Despite AI’s transformative impact on banking operations, its distinctive characteristics present considerable accountability challenges. Drawing on accountability theory and the IT implementation model, we develop a four-dimensional framework (motivators, challenges, practices, impacts) that is based on the findings from semi-structured interviews within the case company. We identified key elements of the AI accountability implementation process, including regulatory compliance, operation control, and risk management as primary motivators; technical knowledge deficits and system opacity as significant challenges; cross-functional integration, risk assessment frameworks, and phased deployment as essential practices; cultural transformation, process reconfiguration, and the evolution of accountability structures as noteworthy impacts. This study contributes to literature by extending the boundaries of the IT implementation model into AI governance and offers guidance for managing AI in high-risk environments.
Recommended Citation
Tong, Jiawei; Marx, Julian; Turel, Ofir; and Cui, Tingru, "Understanding AI Accountability Implementation in Organisations: Insights from a Chinese Commercial Bank" (2025). PACIS 2025 Proceedings. 5.
https://aisel.aisnet.org/pacis2025/it_strategy/it_strategy/5
Understanding AI Accountability Implementation in Organisations: Insights from a Chinese Commercial Bank
This study investigates the implementation process of artificial intelligence (AI) accountability through an in-depth case study of a Chinese commercial bank. Despite AI’s transformative impact on banking operations, its distinctive characteristics present considerable accountability challenges. Drawing on accountability theory and the IT implementation model, we develop a four-dimensional framework (motivators, challenges, practices, impacts) that is based on the findings from semi-structured interviews within the case company. We identified key elements of the AI accountability implementation process, including regulatory compliance, operation control, and risk management as primary motivators; technical knowledge deficits and system opacity as significant challenges; cross-functional integration, risk assessment frameworks, and phased deployment as essential practices; cultural transformation, process reconfiguration, and the evolution of accountability structures as noteworthy impacts. This study contributes to literature by extending the boundaries of the IT implementation model into AI governance and offers guidance for managing AI in high-risk environments.
Comments
Strategy