Catalogue of AI Tools & Metrics
These tools and metrics are designed to help AI actors develop and use trustworthy AI systems and applications that respect human rights and are fair, transparent, explainable, robust, secure and safe.
Why we need a Catalogue of AI tools and metrics to promote trustworthy AI
Explainability, transparency and avoiding bias are among the most critical challenges for AI practitioners, as complex AI systems and algorithms can make these hard to attain. There are many tools at the disposal of AI practitioners and policy makers but it is not always easy to find them and even more difficult to know which ones are the most effective. The Catalogue provides the much-needed space where anyone can find and share tools and methods for making AI trustworthy.
How do we define “trustworthy”? This refers to AI systems that respect OECD AI Principles such as promoting shared wellbeing and prosperity while protecting individual rights and democratic values.
In this context, “tools” is an umbrella term that covers almost anything that helps make AI more trustworthy, from computer software and programming code to employee workshops and training or guidelines and standards. This Catalogue gives access to the latest tools but also use cases about user experiences and metrics.
A public space for AI practitioners to share and compare tools and metrics for trustworthy AI
The Catalogue is a platform where AI practitioners from all over the world can share and compare tools and build upon each other’s efforts to create global best practices and speed up the process of implementing the OECD AI Principles.
Share your tools and methods
If you have or know of any tools to help make AI trustworthy, please submit them to this Catalogue. You can also share your experience using a tool by submitting a use case and share metrics for measuring trustworthy AI.
Video: What are tools for implementing trustworthy AI?
Technical, procedural and educational tools for trustworthy AI
The OECD AI expert group on tools & accountability identified three categories of tools:
- Technical tools address AI-related issues such as bias detection, transparency and explainability, performance, robustness, safety and security against attacks. They include toolkits, software, technical documentation, certification and standards, product development or lifecycle tools, and technical validation tools.
- Procedural tools provide operational and process-related guidance. They include guidelines, governance frameworks, product development methods, lifecycle, and risk management tools, sector-specific codes of conduct and collective agreements, process certifications and standards.
- Educational tools cover all means for building awareness, such as preparing and upskilling stakeholders involved in or affected by the implementation of an AI system. They include change management processes, capacity and awareness-building tools, guidance for designing inclusive AI systems, training programmes and educational materials.
People share their experiences with tools through use cases
The Catalogue allows users to submit their experiences as use cases, where they can give guidance, insights and a general appreciation of the tool. The use cases are linked to the tools they evaluate for easy access.
Metrics and benchmarks to evaluate trustworthiness
How a tool is evaluated for trustworthiness depends upon what it does and the outcomes it produces. Metrics and methodologies exist for measuring and evaluating AI trustworthiness and AI risks. These metrics are often represented through mathematical formulas that assess the technical requirements for achieving trustworthy AI in a particular context. They can ensure that a system is fair, accurate, explainable, transparent, robust, safe, or secure. Anyone can submit their equations and metrics to be part of the Catalogue.
How the information in the Catalogue is compiled and kept up to date
The Catalogue of AI tools & metrics has mechanisms to ensure that content is accurate and up to date. It operates with an open submission process, where tools are submitted directly by the organisations or individuals who created them, and by third parties (see Catalogue disclosures). Submissions are vetted by the OECD Secretariat to ensure accuracy and objectivity.
There is a biannual review and updating process when organisations are encouraged to submit new initiatives and update existing ones. If an existing initiative isn’t updated over a two-year period, it will be removed from the Catalogue. Partnerships with relevant stakeholders – including Business at the OECD, the OECD Civil Society Information Society Advisory Council and the OECD Trade Union Advisory Committee – facilitate this biannual review.
What taxonomy does the OECD use to categorise the tools?
The taxonomy to classify tools for trustworthy AI is largely based on the framework to evaluate approaches to trustworthy AI developed by the OECD.AI expert group on tools & accountability. To learn more about the framework, please read the OECD report on “Tools for trustworthy AI: A framework to compare implementation tools for trustworthy AI systems”.
The Catalogue is the result of a broad partnership that includes the US National Institute of Standards and Technology (NIST), the European Commission (EC), the UK’s AI Standards Hub, Partnership on AI and the Institute for Future Initiatives at the University of Tokyo. Other partners include stakeholder groups like Business at the OECD (BIAC), the OECD Civil Society Information Society Advisory Council (CSISAC) and the OECD Trade Union Advisory Committee (TUAC).
Additionally, the catalogue has benefited greatly from the partnership with Duke’s Ethical Technology Practicum programme. In particular, we are grateful for the contributions of Amanda Booth, Anders Liman, Jacob Stotser and Nathan Gray, under the leadership of Prof. Lee Tiedrich.