arXiv:2607.06656v1 Announce Type: new Abstract: Machine learning models are often intended to augment rather than replace human decision makers, by providing information that is complementary to human judgement. Yet, in practice, human decision makers routinely fail to realize such complementary gains, even when models provide useful signal. In this work, we study how asymmetric information about the quality of information available to a human decision maker vs. an AI impacts the ability of a decision maker to extract complementary value from AI predictions. We show that a key factor is the er
The proliferation of AI models in decision-making contexts highlights the urgent need to understand and optimize human-AI collaboration for effective outcomes.
Organizations investing heavily in AI for augmentation must address the challenges of extracting genuine complementary value, especially concerning asymmetric information and human trust.
This research emphasizes the critical role of understanding information asymmetry between humans and AI, moving beyond simply providing AI predictions to optimizing the human-AI interface for robust complementarity.
- · AI product designers
- · Organizations deploying AI
- · Human decision-makers using AI
- · AI models deployed without human-interface optimization
- · Organizations failing to integrate AI effectively
Improved design principles for human-AI interaction will emerge, focusing on transparency and understanding of AI's informational scope.
Enhanced human trust and adoption of AI systems will lead to greater efficiency and potentially new forms of collaborative intelligence.
These insights could inform regulatory frameworks around AI deployment, particularly in sensitive decision-making domains, promoting explainability and appropriate human oversight.
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Read at arXiv cs.LG