Resource Library

Your go-to hub for the latest updates and insights on ethical AI practices and AI testing tools.
Model Governance Framework for Generative AI
The Model AI Governance Framework for Generative AI (MGF for GenAI) outlines 9 dimensions to create a trusted environment – one that enables end-users to use Generative AI confidently and safely, while allowing space for cutting-edge innovation. Recognising that no single intervention is enough to address existing and emerging AI risks, the framework offers a set of practical suggestions that apply as initial steps.
Project Moonshot Overview
Read the primer on Project Moonshot to learn about our evaluation toolkit for Large Language Models.
New crosswalk with ISO/IEC 42001: 2023 shows international alignment
AI Verify is an AI governance testing framework and software toolkit. AI Verify aims to help companies be more transparent about their AI systems, building trust through standardised tests and strengthening organisational processes.

“ISO/IEC 42001:2023 is a first of its kind international standards designed to ensure broad responsible adoption of AI. The novel approach, in conjunction with the family of standards that ISO/IEC JTC 1/SC 42 is developing, provides a portfolio of international standards that countries and regions can rely on to enable trustworthy and transparent AI.

This mapping between AI Verify Framework and ISO/IEC 42001 demonstrates Singapore’s strong support in advancing global harmonisation in a practical way. It also shows the growing number of countries that are leveraging ISO/IEC 42001 to enable trustworthy AI adoption.”

– Wael William Diab, Chair, ISO/IEC JTC1 / SC42 on AI standards

Both frameworks share a common goal in enabling organisations to strengthen their AI governance implementation. This crosswalk shows how the controls in ISO/IEC 42001:2023 are mapped to the process checks in AI Verify testing framework. Organisations can use AI Verify toolkit to strengthen their AI governance, and practically demonstrate alignment with ISO/IEC 42001:2023, without onerous cost.
We invite interested organisations to use AI Verify to meet the common outcomes of both frameworks and share your use case with us.
Catalogue of LLM Evaluations
The Catalogue of Large Language Model (LLM) Evaluations sets out a comprehensive taxonomy that organises different domains of LLM evaluations to provide organisations a holistic overview of the available tests today.
It seeks to contribute to global discussions on safety standards by recommending a minimum baseline set of safety evaluations that LLM developers should conduct prior to LLM release.

The AI Verify Foundation welcomes initial comments and feedback on this draft release, which can be sent to [email protected]. We are currently establishing a more convenient method to receive and incorporate feedback from the community, and will update in due course.

Crosswalk NIST AI Risk Management Framework and Singapore AI Verify testing framework
This crosswalk will allow companies to use AI Verify to achieve the desired outcomes of both AI Verify testing framework and US NIST AI Risk Management Framework in promoting trustworthy and responsible AI.
The development of the crosswalk is an important step towards harmonisation of international AI governance frameworks to reduce industry’s cost to meet multiple requirements. The joint effort also signals Singapore’s and the US’ common goal of balancing AI innovation, maximising benefits of AI technology while mitigating technology risks.
We invite interested organisations to use AI Verify to meet the common outcomes of both frameworks and share your experience with us.
AI Verify overview
Read the primer of AI Verify to learn about our AI governance testing framework and software toolkit.
Discussion paper
This paper from Singapore’s IMDA and Aicadium raises for discussion policy ideas on building an ecosystem for trusted and responsible adoption of AI, in a way that encourages a positive loop – to spur innovation and tap on opportunities afforded AI, ever more so with the advent of Generative AI.
By sharing ideas on practical pathways for governance, the paper seeks to enhance discourse and foster greater global collaboration to ensure AI is used in a safe and responsible manner, and that the most critical outcome – trust – is sustained.

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Your organisation’s background – Could you briefly share your organisation’s background (e.g. sector, goods/services offered, customers), AI solution(s) that has/have been developed/used/deployed in your organisation, and what it is used for (e.g. product recommendation, improving operation efficiency)?


Your AI Verify use case – Could you share the AI model and use case that was tested with AI Verify? Which version of AI Verify did you use?


Your reasons for using AI Verify – Why did your organisation decide to use AI Verify?


Your experience with AI Verify – Could you share your journey in using AI Verify? For example, preparation work for the testing, any challenges faced, and how were they overcome? How did you find the testing process? Did it take long to complete the testing?


Your key learnings and insights – Could you share key learnings and insights from the testing process? For example, 2 to 3 key learnings from the testing process? Any actions you have taken after using AI Verify?


Your thoughts on trustworthy AI – Why is demonstrating trustworthy AI important to your organisation and to any other organisations using AI systems? Would you recommend AI Verify? How does AI Verify help you demonstrate trustworthy AI?
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