Integrators at War: Mediating in AI-assisted Resort-to-Force Decisions

TL;DR

This paper examines the role of integrators in mediating AI-assisted military decisions, identifying challenges and proposing policies to improve human-machine teaming in resort-to-force contexts.

Contribution

It conceptualizes the sociotechnical system involving developers, integrators, and users, highlighting the integrators' mediating role and addressing integration challenges in AI-enabled military decisions.

Findings

Identifies challenges from technology, roles, and interactions in AI military systems.

Highlights the importance of integrators in linking developers and users.

Provides policy recommendations for better AI integration in military decision-making.

Abstract

The integration of AI systems into the military domain is changing the way war-related decisions are made. It binds together three disparate groups of actors - developers, integrators, users - and creates a relationship between these groups and the machine, embedded in the (pre-)existing organisational and system structures. In this article, we focus on the important, but often neglected, group of integrators within such a sociotechnical system. In complex human-machine configurations, integrators carry responsibility for linking the disparate groups of developers and users in the political and military system. To act as the mediating group requires a deep understanding of the other groups' activities, perspectives and norms. We thus ask which challenges and shortcomings emerge from integrating AI systems into resort-to-force (RTF) decision-making processes, and how to address them. To answer this, we proceed in three steps. First, we conceptualise the relationship between different groups of actors and AI systems as a sociotechnical system. Second, we identify challenges within such systems for human-machine teaming in RTF decisions. We focus on challenges that arise a) from the technology itself, b) from the integrators' role in the sociotechnical system, c) from the human-machine interaction. Third, we provide policy recommendations to address these shortcomings when integrating AI systems into RTF decision-making structures.