Evaluation Perspective Guide on AI Safety (Version 1.01)
AI セーフティに関する評価観点ガイド（第1.01版）
This guide provides basic considerations and evaluation perspectives for individuals involved in the development and provision of AI systems, specifically focusing on Large Language Model (LLM) systems, to conduct AI safety evaluations. It covers ten evaluation perspectives including harmful content control, prevention of misinformation, fairness, and security, drawing from domestic guidelines and international trends. The document outlines evaluation timing across development, deployment, and usage phases, as well as technical and management evaluation methodologies.
Summary and content fields are drawn from the document text and are pending editorial review.
Who issued it
Who it governs
This guide is for AI developers, providers, management supervisors, and business executives involved in building and deploying AI systems who need to conduct safety evaluations.
What the document contains
The document covers the definition of AI safety, ten detailed evaluation perspectives (e.g., harmful information output control, prevention of misinformation, fairness, privacy, security, explainability, robustness, data quality, and verifiability), evaluation executors, evaluation timing across lifecycle phases, and evaluation methodologies.
The document does not provide binding legal penalties, exhaustive multi-modal evaluation benchmarks, or detailed technical implementation scripts.
- The guide establishes ten specific evaluation perspectives for assessing AI safety, primarily targeting LLM systems.
- Key elements of AI safety include human-centric principles, safety, fairness, privacy protection, security, and transparency.
- Evaluations should be conducted across development, deployment, and usage phases by appropriate internal or external roles.
- Both technical evaluations (such as tool-based evaluations and red teaming) and management evaluations are necessary.
- The document references the Japanese AI Business Guidelines and international frameworks to align with global standards.
1 はじめに 2 AI セーフティ 3 評価観点の詳細 4 評価実施者及び評価実施時期 5 評価手法の概要 6 評価に際しての留意事項
How it scores
Each score is backed by a quote verified against the document text. Editorial review has not yet confirmed them.
Enforcement
0/5Consequences and compliance mechanisms with real force
screening score · 2026-10-25