Beyond the Ethical Compass: Reliability Challenges in Adaptive Human-AI Teaming

Traditionally, the ethical compass guides human decisions, often in contradiction to technically best solutions (e.g., AI-only decisions with low error probability). Studies show that human-AI combinations do not automatically generate synergies; on average, they perform worse than humans or AI alone. The decision of when humans should trust superior algorithms is particularly critical. Human augmentation and synergies in generating tasks are positive. Meta-analyses (106 studies) confirm that performance and reliability depend on tasks and the relative strengths of both. Advantages only arise in certain situations or combinations; wrong decisions occur when AI outperforms humans. Effective cooperation requires situational adaptation and understanding of the error structure. When ethical criteria are integrated into AI systems, the focus shifts to functional reliability related to the constellations: human-only, AI-only, and human-AI teaming (HAT). Dynamic workflows and flexible teaming structures enable adaptive collaboration. A key question is which methods or combination of methods will describe adequately the merged hardware reliability, functional reliability of AI, and the reliability of supervising humans. The paper conceptually outlines the fundamental challenges associated with the following research areas, which become relevant in this context: 1. Reliability-Aware Adaptive HAT: Situational AI adaptation (goals, KBI, emotions) and human interpretation 2. Situated Workflow Control: Integration of knowledge and situational control 3. Error and Knowledge Bases: Workflow knowledge, individual errors, and fundamental human and AI error structures to improve reliability 4 . Reliability-oriented Metrics and Evaluation: Reliability of humans/AI and task type (decision vs. generation-oriented) are decisive; XAI/confidence alone does not provide significant synergy 5 . New Reliability Descriptions: Methods for combined reliability of humans, AI, and hardware are required. Based on existing findings, this paper provides firstly a related structured approach to a reliability-oriented human-AI teaming task and presents the methodology both conceptually and through examples. In conclusion, evidence-based human-AI teaming systems require dynamic adaptation, situational workflow control, and robust reliability metrics. Reliability becomes a central design goal, extending beyond ethical control.

Keywords: Human-AI Teaming, Adaptive Human-AI Teaming, Resilient Human-AI Collaboration, HAT Reliability, Human-AI Synergy, Situated Workflow Control.