Accountability in Autonomous Drone-Based Firefighting: Insights From a Field Trial

There is a growing research field exploring how autonomous drones can enhance emergency response effectiveness. Integrating these (artificial) agents into existing emergency teams and workflows may significantly impact established accountability relationships. This paper examines how autonomous drones affect accountability attribution within complex socio-technical systems. Drawing on two real-life field trials in firefighting, the study reveals substantial uncertainty around accountability when drones are organizationally deployed. Using Bovens' accountability framework, two challenges are identified: (1) uncertainty about the role of drones within hierarchical structures, leading to confused accountability ascriptions; and (2) new forms of human-drone interactions introducing additional accountability-relevant issues. Based on these insights, the paper proposes actionable recommendations to support the responsible integration of autonomous drones into firefighting operations without undermining accountability. These findings offer practical guidance for policymakers and contribute to further research on accountability in autonomous systems.

From Ad Hoc Pilots to Repeatable Patterns: Structuring Drone Collaboration in Emergency Services with DroneLets

Drones hold promise for supporting emergency services, but their integration into workflows remains ad hoc and coordination-intensive. This paper addresses two research questions: how emergency teams want to collaborate with drones, and how to formalize these collaborations into repeatable processes. Based on four field trials and 95 interviews, we derive 44 interaction patterns grouped into 10 meta-patterns reflecting operational needs such as reconnaissance, communication, and logistical support. To structure these practices, we introduce DroneLets - a new class of design artifacts that extend Collaboration Engineering to embodied agents. DroneLets capture setup requirements, drone capabilities, environmental constraints, and coordinated actions across human and drone actors. They offer a modular framework for designing repeatable, scalable collaboration processes in emergency services, illustrated through patterns such as broadcasting to bystanders and post-fire monitoring. This work expands the scope of CE and provides a structured foundation for integrating autonomous drones into high-stakes field operations.

Learning with Digital Agents: An Analysis based on the Activity Theory

Digital agents are considered a general-purpose technology. They spread quickly in private and organizational contexts, including education. Yet, research lacks a conceptual framing to describe interaction with such agents in a holistic manner. While focusing on the interaction with a pedagogical agent, i.e., a digital agent capable of natural-language interaction with a learner, we propose a model of learning activity based on activity theory. We use this model and a review of prior research on digital agents in education to analyze how various characteristics of the activity, including features of a pedagogical agent or learner, influence learning outcomes. The analysis leads to identification of IS research directions and guidance for developers of pedagogical agents and digital agents in general. We conclude by extending the activity theory-based model beyond the context of education and show how it helps designers and researchers ask the right questions when creating a digital agent.