arXiv:2605.12646v2 Announce Type: replace-cross Abstract: It is widely agreed that when AI models assist decision-makers in high-stakes domains by predicting an outcome of interest, they should communicate the confidence of their predictions. However, empirical evidence suggests that decision-makers often struggle to determine when to trust a prediction based solely on this communicated confidence. In this context, recent theoretical and empirical work suggests a positive correlation between the utility of AI-assisted decision-making and the degree of alignment between the AI confidence and th
The proliferation of AI in high-stakes domains necessitates methods for ensuring human trust and effective decision-making, which current confidence-communication methods often fail to achieve.
Understanding how to effectively align human decision-making with AI assistance determines the practical utility and adoption of AI systems in critical applications, impacting efficiency and safety.
The focus shifts from merely providing AI confidence scores to understanding and engineering human-AI alignment, which could lead to more robust and trustworthy AI-assisted systems.
- · AI developers focused on human-centered design
- · High-stakes industries (e.g., healthcare, finance, defense) adopting AI
- · Regulatory bodies developing AI guidelines
- · AI systems with poor interpretability
- · Decision-makers relying solely on simple confidence metrics
- · Organizations implementing AI without human interface considerations
Improved human-AI collaboration and trust in critical decision-making processes.
Accelerated adoption of AI in previously hesitant sectors due to enhanced reliability and safety.
The emergence of new AI design paradigms prioritizing human-AI alignment, leading to more ethical and effective autonomous systems.
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Read at arXiv cs.AI