Adaptations to Trust Incidents with Artificial Intelligence

Abstract

Artificial Intelligence (AI) is becoming ubiquitous in national security work (intelligence, defense, etc.); however, introducing AI into work systems is fraught with challenges. Trust is gained and lost through experiences, and there are many factors that affect trust in AI. Similarly, users adapt their workflows based on trust in these systems. We used a naturalistic approach to understand how intelligence professionals adapted their work practices after gaining or losing trust in AI. We found a variety of adaptations, which were characterized as either being task-based or frequency-based; where users added or removed tasks from their workflow or where they changed the frequency in which they used the AI in their workflow, respectively. We provide specific examples and quotes from participants along with findings, and discuss potential methodological implications for studying and designing AI-driven work systems.