Abstract

The concept of shared responsibility has become pivotal in the Information Systems (IS) community, yet it remains inconsistently defined and applied. This paper addresses this gap by developing a transdisciplinary synthesis model that clarifies the core variables of shared responsibility and examines how different forms of Artificial Intelligence (AI)—particularly traditional AI and Generative AI—shape its dynamics. Using the Gioia methodology, we identified three key dimensions: Technological Adoption and Resistance, Information Control, and Attitude to Knowledge Sharing. Our analysis shows that traditional AI, while offering robust analytical capabilities, tends to centralize expertise and control, thereby complicating accountability. Generative AI, in contrast, democratizes access to advanced tools but introduces opacity and unpredictability, challenging trust and responsibility allocation. These findings were validated through semi-structured interviews with experts from ten organizations across different industries and roles. The study contributes by offering (1) a literature-based definition of shared responsibility in IS, (2) a conceptual framework that highlights how traditional AI and Generative AI differently shape responsibility attribution, and (3) implications for designing transparent and trust-worthy socio-technical systems. This work provides valuable insights for IS researchers and practitioners and calls for future research on decentralized governance mechanisms and methods to quantify the shared value created by AI.

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