Xin (Robert) Luo, Hong Kong Metropolitan University

Tingru Cui, University of Melbourne

Kun Chen, Shenzhen University

Artificial intelligence (AI) is increasingly embedded in decision support systems, shaping decisions across a wide range of contexts including hiring, compliance review, financial risk assessment, healthcare triage, scheduling, resource allocation, fraud detection, service operations, and platform governance (Qin et al. 2023; Storey et al. 2024). As AI systems become more capable, they are no longer limited to supporting human judgment through data processing or prediction. Instead, decision-related tasks are increasingly delegated to AI, including information filtering, prioritization, classification, recommendation generation, monitoring, anomaly detection, and partial decision execution. These developments extend beyond formal organizational settings and are increasingly evident in everyday decision-making, community governance, and digitally mediated social interactions (Strunk et al. 2025).

This growing delegation of decision work to AI raises a fundamental question for decision support research: how can accountability be maintained when decision-making is distributed across humans, AI systems, data infrastructures, and social and institutional arrangements? While accountability is frequently invoked in discussions of responsible AI, algorithmic governance, and digital ethics, its meaning remains fragmented and underdeveloped in research on AI-enabled decision support (Barocas and Boyd 2017; Diakopoulos 2016; Glikson and Woolley 2020). Prior work has advanced important insights into algorithmic decision-making, fairness, trust, transparency, and human-AI collaboration. However, comparatively less attention has been paid to accountability as a sociotechnical property that emerges across multiple levels, including individual judgment, community norms, and broader governance structures (Bansal et al. 2021; Storey et al. 2024).

We propose this special issue to develop a decision support perspective on AI accountability. We define AI accountability in decision support as the sociotechnical capacity to assign, justify, monitor, review, and repair responsibility for decisions in which AI systems participate. This definition shifts attention from AI systems as isolated technical artifacts to the broader configurations through which AI-supported decisions are produced, interpreted, accepted, contested, overridden, escalated, documented, evaluated, and corrected. From this perspective, accountability is not merely a technical feature or regulatory requirement. It is a distributed capability embedded in system design, decision practices, social norms, and governance mechanisms that operate across individuals, communities, and institutional contexts.

A central concern of this special issue is delegation. When decision-related tasks are delegated to AI, authority, expertise, discretion, and responsibility are reconfigured across human and machine actors (Baird and Maruping 2021; Liu et al. 2025). AI systems may be tasked with ranking applicants, identifying risks, detecting anomalies, recommending actions, or moderating digital content. These forms of delegation reshape how decisions are made and experienced, altering who is expected to identify errors, who has the authority to intervene, who must justify outcomes, and who is held responsible when decisions produce unintended consequences (Adam et al. 2024). Importantly, these dynamics unfold not only within formal institutions but also in distributed settings such as online communities, platform ecosystems, and everyday decision environments.

This special issue therefore aims to explore how accountability can be designed, enacted, and evaluated in AI-enabled decision support systems across diverse contexts. How should decision right be allocated between humans and AI? How can oversight and intervention be meaningfully supported when AI systems influence or shape decisions? How can interfaces, interaction processes, and governance mechanisms enable responsible reliance while preserving human judgment? How can documentation, traceability, and feedback mechanisms support the identification of errors, the assignment of responsibility, and the improvement of future decisions? More broadly, how do accountability conditions influence trust, reliance, resistance, and decision quality in human-AI interaction?

Prior DSS and IS research has examined important aspects of AI-enabled decision support, including algorithmic decision support in employee performance evaluation (Qin et al. 2023), AI-enabled credit scoring and financial inclusion (Li et al. 2024), human trust in AI (Glikson and Woolley 2020), human-AI team performance (Bansal et al. 2021), and the design of human-AI systems in decision sciences (Storey et al. 2024). Recent work has also highlighted the importance of understanding AI-enabled systems as organizational and sociotechnical arrangements rather than purely technical tools. Building on this foundation, the proposed special issue focuses specifically on how accountability is created, distributed, and sustained when AI systems become active participants in organizational decision-making.

The proposed issue is strongly aligned with the aims and scope of Decision Support Systems because it directly contributes to core areas of decision support research, including system design, implementation, evaluation, behavioral foundations, organizational impact, and enhanced decision-making. It also provides a timely opportunity to extend decision support scholarships beyond traditional concerns with accuracy, efficiency, usability, and user acceptance toward a richer understanding of accountability in AI-mediated organizational decisions.

This special issue welcomes rigorous empirical, conceptual, computational, design science, and mixed-method studies that examine how AI-enabled decision support systems can be designed, implemented, governed, and evaluated to support accountable decision-making.

By placing accountability and delegation at the center of AI-enabled decision support, this special issue aims to develop a coherent and distinctive research agenda for DSS scholarship. It will advance theoretical understanding of accountability in information systems, generate practical guidance for organizations deploying AI in decision-making, and provide design insights for building more responsible and effective decision support systems. More broadly, it will help IS and decision support researchers address one of the most pressing questions in contemporary AI-enabled organizing: not only whether AI can improve decisions, but also how organizations can remain responsible for the decisions that AI helps to shape.

Potential topics

• conceptual foundations of accountability in AI-enabled decision support systems
• delegation of decision tasks and decision rights to AI systems
• allocation of authority and responsibility between humans and AI
• human oversight, intervention, escalation, and override in AI-supported decisions
• accountability mechanisms in AI-enabled DSS design
• audit trails, documentation, responsibility tracking, and governance infrastructures
• role-based accountability across users, managers, technical teams, vendors, regulators, and affected stakeholders
• AI accountability in analytics, healthcare, finance, compliance, platform governance, public services, and operations
• decision quality, fairness, trust, reliance, resistance, and organizational learning in AI-supported decisions
• accountability pressures and their effects on human-AI collaboration
• organizational consequences of AI delegation for work, authority, autonomy, and professional judgment
• evaluation frameworks and metrics for accountable AI-enabled DSS
• design science approaches to responsible and accountable AI-enabled decision support

Timeline

• May 1, 2027: Submissions open
• September 30, 2027: Submission deadline
• Initial decisions issued on a rolling basis, with most first decisions targeted within 3 months of submission
• Target publication window: once the final version is accepted