AI Evaluation Should Work With Humans
A recent position paper critiques the prevailing AI evaluation framework, which emphasizes superhuman autonomous capabilities and implicitly aims to replace human roles, suggesting it misguides AI progress. The author advocates for a shift towards assessing the effectiveness of human-AI collaborations instead. This transition is believed to cultivate AI systems that genuinely enhance human abilities, ultimately yielding superior societal benefits compared to the existing approach. Classified under Computer Science > Artificial Intelligence, the paper was submitted to arXiv with the identifier 2608.13577. It includes a history of submissions and references and is linked to arXivLabs, which promotes collaborative projects based on principles of openness, community, excellence, and privacy of user data. No authors, institutions, or locations are mentioned.
Key facts
- The paper is a position paper on AI evaluation.
- It argues against the dominant paradigm of evaluating AI for superhuman autonomous performance.
- It proposes evaluating human-AI team performance instead.
- The goal is to foster AI systems that complement human capabilities.
- The paper is categorized under Computer Science > Artificial Intelligence.
- It was submitted to arXiv with identifier 2608.13577.
- arXivLabs is mentioned as a framework for collaborative projects.
- The paper emphasizes values of openness, community, excellence, and user data privacy.
Entities
Institutions
- arXiv