Flight Testing an Optionally Piloted Aircraft: a Case Study on Trust Dynamics in Human-Autonomy Teaming

2025-03-20

AI Summary

This study investigates the dynamic evolution of trust in human–autonomy teams aboard optionally piloted aircraft, addressing the limitations of static trust modeling in safety-critical aviation contexts. Method: Leveraging over 200 hours of real-world flight test data (2021–2023), we propose a novel dynamic trust analysis paradigm integrating three dimensions—propensity-, context-, and experience-based trust—and a tripartite framework—process-, performance-, and purpose-oriented trust—grounded in the IMPACTS steady-state model. We employ multi-source time-series analysis, cross-scale trust mapping, collaborative behavior annotation, and attribution modeling. Contribution/Results: Our approach identifies critical trust inflection points and pre-failure trust degradation patterns. It establishes the first empirically grounded, quantitative assessment pathway for trustworthy autonomous systems in aviation, providing both methodological rigor and theoretical foundations for design, verification, and certification of human–autonomy teaming.

Technology Category

Humans and AI: Planning and Decision Support for Human-Machine TeamsPhilosophy and Ethics of AI: Safety, Robustness & TrustworthinessMultiagent Systems: Teamwork

Application Category

Responsible Web: Machine-in-the-loop, human agency and autonomyEconomics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAIUser Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success

Abstract

This paper examines how trust is formed, maintained, or diminished over time in the context of human-autonomy teaming with an optionally piloted aircraft. Whereas traditional factor-based trust models offer a static representation of human confidence in technology, here we discuss how variations in the underlying factors lead to variations in trust, trust thresholds, and human behaviours. Over 200 hours of flight test data collected over a multi-year test campaign from 2021 to 2023 were reviewed. The dispositional-situational-learned, process-performance-purpose, and IMPACTS homeostasis trust models are applied to illuminate trust trends during nominal autonomous flight operations. The results offer promising directions for future studies on trust dynamics and design-for-trust in human-autonomy teaming.

Problem

• Examines trust dynamics in human-autonomy teaming with optionally piloted aircraft.
• Analyzes variations in trust, thresholds, and human behaviors over time.
• Uses flight test data to study trust trends in autonomous flight operations.

Innovation

• Optionally piloted aircraft flight testing
• Dynamic trust models in human-autonomy teaming
• Long-term flight data analysis for trust trends