The AI Risk Ontology (AIRO) is an open-source formal ontology that provides a minimal set of concepts and relations for modelling AI use cases and their associated risks. AIRO has been developed according to the requirements of the EU AI Act and international standards, including ISO/IEC 23894 on AI risk management and ISO 31000 family of standards.

Vectice is a regulatory MLOps platform for AI/ML developers and validators that streamlines documentation, governance, and collaborative reviewing of AI/ML models. Designed to enhance audit readiness and ensure regulatory compliance, Vectice automates model documentation, from development to validation. With features like automated lineage tracking and documentation co-pilot, Vectice empowers AI/ML developers and validators to work in their favorite environment while focusing on impactful work, accelerating productivity, and reducing risk.

Enzai’s EU AI Act Compliance Framework makes achieving compliance with the world’s first comprehensive AI governance legislation as easy as possible. The framework breaks hundreds of pages of regulations down into easy-to-follow steps allowing teams to enable organisations to seamlessly and confidently assess the compliance of their AI Systems with the Act and complete the requisite conformity assessments.

The Algorithmic Transparency Recording Standard (ATRS) is a framework for capturing information about algorithmic tools, including AI systems. It is designed to help public sector bodies openly publish information about the algorithmic tools they use in decision-making processes that affect members of the public.

Use Saidot to empower AI teams to do high-quality AI governance efficiently and with quality

AI assurance as smart as your AI systems

This paper addresses the complex and contentious issues surrounding AI governance, focusing on organizing overarching and systematic approaches for the transparency of AI algorithms, and proposes a practical toolkit for implementation. Designed to be highly user-friendly for both businesses and government agencies engaging in self- and co-regulation, the toolkit presents disclosure items in a list format, allowing for discretion and flexibility when considering AI algorithm providers, users, and risks.

This app matches information in model cards to proposed regulatory compliance descriptions in the EU AI Act. This is a prototype to explore the feasibility of automatic checks for compliance, and is limited to specific provisions of Article 13 of the Act, “Transparency and provision of information to users”.

Enterprise platform for governance, risk and compliance in AI. Regulatory compliance with global laws intersecting with AI.

Fairness metric selection tool for AI applications.

Automated AI testing, monitoring, and governance

An accessible framework (single A3 sheet) for governing the ethical development and use of AI - including the what (ethic), how (realisation principals), and submission of evidence for approval. Trustworthiness and bias are also covered

Zupervise is a unified risk transparency platform for AI governance.

AI governance, risk, and compliance software platform that helps organizations understand and manage the risks associated with AI, while meeting their evolving regulatory obligations

This playbook is a legal research resource for various activities related to data gathering, data governance, and disposition of an AI model available as a public resource. It aims to benefit academic and government researchers including those in New York State who wish to understand how best to use AI models to provide natural language processing (“NLP”) as public infrastructure, but who do not have legal resources.

Model cards are the tool for transparent AI documentation. Model cards are essential for discoverability, reproducibility, and sharing. You can find a model card as the README.md file in any model repo. Under the hood, model cards are simple Markdown files with additional metadata.

A documentation-first, human-centered data collection project as part of the BigScience initiative. A geographically diverse set of target language groups were identified (Arabic, Basque, Chinese, Catalan, English, French, Indic languages, Indonesian, Niger-Congo languages, Portuguese, Spanish, and Vietnamese, as well as programming languages) for which to collect metadata on potential data sources. To structure this effort, an online catalogue was developed as a supporting tool for gathering metadata through organized public hackathons.