Overreliance

Mitigation strategy

1. Prioritize Model Calibration and Uncertainty Expression: Modify model training processes, such as reward functions, to accurately reflect and communicate epistemic uncertainty (e.g., confidence scores or natural language markers) to prevent users from overestimating system reliability and competence. 2. Implement Cognitive Forcing Functions and Verification Cues: Design system interfaces to incorporate friction mechanisms (e.g., extra clicks, confirmation dialogues) and priming statements that clarify system limitations, thereby interrupting automation bias and promoting user verification at critical decision points. 3. Establish Human-in-the-Loop Oversight and Continuous Learning Protocols: Mandate human oversight for critical AI-driven decisions (Human-in-the-Loop systems) and institute mandatory professional development to maintain and enhance fundamental cognitive and domain-specific skills to counteract skill atrophy.