Establishment of an AI Safety Foundation to Support Safe Collaboration Between Humans and AI
Under a project promoted by NEDO, five organizations including AIST have developed and published the 'Multimodal AI Quality Management Guideline' and evaluation protocols to ensure the safety of AI systems. This outcome serves as a common reference for businesses to implement risk measures at every stage from AI planning to operation, aiming to accelerate the development of AI safety foundations.
📋 Article Processing Timeline
- 📰 Published: May 28, 2026 at 10:00
- 🔍 Collected: June 1, 2026 at 01:09 (87h 9m after Published)
- 🤖 AI Analyzed: June 1, 2026 at 23:44 (22h 34m after Collected)
In the "Research and Development on AI Safety / Strengthening AI Safety R&D" project (hereinafter referred to as the Project) promoted by NEDO (New Energy and Industrial Technology Development Organization), five organizations—NEDO, the National Institute of Advanced Industrial Science and Technology (AIST), Citadel AI Inc., Copy Inc., and the University of the Ryukyus—have developed and published guidelines and evaluation protocols that serve as a common foundation for ensuring the safety of AI systems.
The guidelines and protocols formulated under this project focus on ensuring safety from the planning and design stages of AI systems through to their evaluation and operation. They organize fundamental concepts and procedures for businesses developing and implementing AI-driven systems to identify risks and consider appropriate countermeasures.
By widely utilizing these guidelines and protocols, the project aims to permeate common concepts and procedures for AI system safety throughout society and accelerate the development of a common AI safety foundation for the secure use of AI.
1. Background
Against the backdrop of initiatives such as the Hiroshima AI Process, which was launched at the 2023 G7 Hiroshima Summit, discussions and structural development regarding AI safety are progressing globally. In response to these international trends, Japan has established the AI Safety Institute (AISI) and is participating in international discussions.
This project supports efforts to promote the creation of international rules for the safe and secure use of generative AI from the perspective of research and development, bringing together government and private sectors. In recent years, as human-AI collaboration in judgment and action increases, responding to the common challenge of how to design, evaluate, and operate AI safety has become essential.
The project conducted research and development aimed at establishing a common foundation for evaluating and operating AI safety. While AI technologies and application fields are diverse, challenges such as designing safe interaction between humans and AI and ensuring safety through judgment, verification, and operation are common across all fields.
The project is structured to develop evaluation and management technologies that serve as a "yardstick" for safety, develop AI safety evaluation and implementation technologies for specific application domains, and organize/systematize these results into a form practical for business use, leading to the formulation of guidelines for AI safety implementation.
2. Key Outcomes
To address the diverse challenges of AI safety, the project developed a wide range of guidelines, evaluation methods, templates, and evaluation environments that span the stages of "design, evaluation, and operation."
(1) Formulation of Multimodal AI Quality Management Guidelines
As the core outcome of the project, AIST has formulated guidelines that organize quality management perspectives and processes for multimodal AI, which receives images and text and responds primarily through text. Focusing on "cross-modal correspondence capability" as a unique evaluation perspective for multimodal AI, the capability is classified into four levels. To ensure the safety and quality of multimodal AI systems, identifying the required level of cross-modal correspondence capability is crucial, and corresponding actions for each stage of the lifecycle have been organized systematically according to these levels.
Furthermore, the guidelines present three case studies: automated image captioning, image diagnosis for aging infrastructure, and content moderation on social media. They highlight points of caution and quality management issues in situations involving human judgment and supervision.
This guideline provides a common design and evaluation framework for ensuring safety and quality based on the characteristics of multimodal AI and serves as a base for practical application of AI safety.
The guidelines and protocols formulated under this project focus on ensuring safety from the planning and design stages of AI systems through to their evaluation and operation. They organize fundamental concepts and procedures for businesses developing and implementing AI-driven systems to identify risks and consider appropriate countermeasures.
By widely utilizing these guidelines and protocols, the project aims to permeate common concepts and procedures for AI system safety throughout society and accelerate the development of a common AI safety foundation for the secure use of AI.
1. Background
Against the backdrop of initiatives such as the Hiroshima AI Process, which was launched at the 2023 G7 Hiroshima Summit, discussions and structural development regarding AI safety are progressing globally. In response to these international trends, Japan has established the AI Safety Institute (AISI) and is participating in international discussions.
This project supports efforts to promote the creation of international rules for the safe and secure use of generative AI from the perspective of research and development, bringing together government and private sectors. In recent years, as human-AI collaboration in judgment and action increases, responding to the common challenge of how to design, evaluate, and operate AI safety has become essential.
The project conducted research and development aimed at establishing a common foundation for evaluating and operating AI safety. While AI technologies and application fields are diverse, challenges such as designing safe interaction between humans and AI and ensuring safety through judgment, verification, and operation are common across all fields.
The project is structured to develop evaluation and management technologies that serve as a "yardstick" for safety, develop AI safety evaluation and implementation technologies for specific application domains, and organize/systematize these results into a form practical for business use, leading to the formulation of guidelines for AI safety implementation.
2. Key Outcomes
To address the diverse challenges of AI safety, the project developed a wide range of guidelines, evaluation methods, templates, and evaluation environments that span the stages of "design, evaluation, and operation."
(1) Formulation of Multimodal AI Quality Management Guidelines
As the core outcome of the project, AIST has formulated guidelines that organize quality management perspectives and processes for multimodal AI, which receives images and text and responds primarily through text. Focusing on "cross-modal correspondence capability" as a unique evaluation perspective for multimodal AI, the capability is classified into four levels. To ensure the safety and quality of multimodal AI systems, identifying the required level of cross-modal correspondence capability is crucial, and corresponding actions for each stage of the lifecycle have been organized systematically according to these levels.
Furthermore, the guidelines present three case studies: automated image captioning, image diagnosis for aging infrastructure, and content moderation on social media. They highlight points of caution and quality management issues in situations involving human judgment and supervision.
This guideline provides a common design and evaluation framework for ensuring safety and quality based on the characteristics of multimodal AI and serves as a base for practical application of AI safety.
FAQ
本プロジェクトの目的は何ですか?
AIシステムの企画から運用までの段階において、安全性確保のための共通基盤(ガイドラインや評価プロトコル)を整備・公開し、AIセーフティの共通的な考え方と手順を社会に浸透させることです。
策定されたガイドラインの対象は何ですか?
画像とテキストを受け取り、主にテキストで応答するマルチモーダルAIを対象としています。
本事業の体制はどうなっていますか?
NEDOが推進し、産総研、株式会社Citadel AI、株式会社コーピー、国立大学法人琉球大学の計5者が連携して研究開発を実施しました。
ガイドラインの特徴は何ですか?
マルチモーダルAI特有の「クロスモーダル照応能力」を4段階に分類し、その水準に応じたライフサイクル別の対応を体系的に整理した点です。
この成果は実務でどのように活用されますか?
AIを活用したシステムを開発・導入する事業者が、リスクの洗い出しや適切な対策検討を行う際の共通的なガイドとして活用できます。