The document compiles evaluation practices aimed at improving internal validity, external validity, and reproducibility of GPAI model assessments. It covers suggestions for human uplift studies, benchmark evaluations, and cross-cutting methods applicable across various evaluation types. These recommendations are structured around four key stages of the evaluation life cycle: design, implementation, execution, and documentation. The guidance draws on established methodologies from diverse fields such as machine learning, statistics, psychology, economics, and biology, which offer valuable insights for AI evaluation. By integrating these interdisciplinary lessons, the document seeks to advance the emerging science of GPAI evaluation. The intended audience includes GPAI model providers who face systemic risks and must comply with regulatory frameworks like the EU AI Act, third-party evaluators conducting assessments, policymakers overseeing evaluation rigor, and academic researchers developing evaluation methodologies. The suggestions aim to foster more reliable, transparent, and reproducible evaluations, which are crucial for managing the risks associated with advanced AI systems. Implementing these practices can help ensure that GPAI models are assessed comprehensively and consistently, supporting safer deployment and informed policymaking. The document encourages ongoing dialogue and refinement of evaluation standards as the field evolves.
👉 Pročitaj original: arXiv AI Papers
