arXiv:2607.03025v1 Announce Type: new Abstract: The use of Large Language Models (LLMs) across diverse areas of human activity-ranging from everyday tasks to safety-critical applications-aims to enhance decision-making effectiveness with minimal human feedback. Concurrently, it seeks to align decisions with human expectations, preferences, and needs while mitigating risks associated with AI non-determinism. However, humans frequently over- or under-rely on AI recommendations, and current AI systems remain poorly calibrated to human expectations. To address these challenges, we introduce a huma
The proliferation of LLMs into various applications necessitates addressing the challenges of human-AI collaboration and trust to ensure effective and safe integration.
This research directly tackles crucial issues of AI reliability, human over- or under-reliance, and alignment with human expectations, which are critical for the broader adoption and utility of AI systems.
A proposed 'human-centric reflective architecture' aims to improve AI calibration and decision-making effectiveness by better integrating human feedback and mitigating AI non-determinism.
- · AI developers
- · Organizations deploying AI
- · Users of AI systems
- · AI systems with poor human-AI interaction
- · Sectors reliant on uncalibrated AI
Improved trust and adoption of AI systems in sensitive decision-making roles.
Faster integration of AI into critical infrastructure and specialized professional fields due to enhanced reliability.
Potentially, a re-evaluation of regulatory frameworks for AI based on new standards for human-AI interaction and accountability.
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Read at arXiv cs.AI