An artificial intelligence (AI) impact assessment tool to provide organisations a method to assess AI systems for compliance with Canadian human rights law. The purpose of this human rights AI impact assessment is to assist developers and administrators of AI systems to identify, assess, minimise or avoid discrimination and uphold human rights obligations throughout the lifecycle of an AI system.

The Infosys Responsible AI toolkit provides a set of APIs to integrate safety, security, privacy, explainability, fairness, and hallucination detection into AI solutions, ensuring trustworthiness and transparency.

PolicyPilot is designed to assist users in creating and managing AI policies, streamlining AI governance with automated compliance monitoring and risk management.

This program provides organisations with a comprehensive, independent review of their AI approaches, ensuring alignment with consensus standards and enhancing trust among stakeholders and the public in their AI practices.

This tool provides a comprehensive risk management framework for frontier AI development, integrating established risk management principles with AI-specific practices. It combines four key components: risk identification through systematic methods, quantitative risk analysis, targeted risk treatment measures, and clear governance structures.

An open-source Python library designed for developers to calculate fairness metrics and assess bias in machine learning models. This library provides a comprehensive set of tools to ensure transparency, accountability, and ethical AI development.

Behavior Elicitation Tool (BET) is a complex-AI system that systematically probes and elicits specific behaviors from cutting-edge LLMs. Whether for red-teaming or targeted behavioral analysis, this automated solution is Dynamic Optimized and Adversarial (DAO) and can be configured to test the robustness precisely and help to have a better control of the AI system.

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.

COMPL-AI is an open-source compliance-centered evaluation framework for Generative AI models

MLPerf Client is a benchmark for Windows and macOS, focusing on client form factors in ML inference scenarios like AI chatbots, image classification, etc. The benchmark evaluates performance across different hardware and software configurations, providing command line interface.

This document addresses how artificial intelligence machine learning can impact the safety of machinery and machinery systems.

ISO/IEC 25023:2016 defines quality measures for quantitatively evaluating system and software product quality in terms of characteristics and subcharacteristics defined in ISO/IEC 25010 and is intended to be used together with ISO/IEC 25010.

This document provides a structured framework for gaining informed consent from individuals before using their copyright works (including posts, articles, or comments), Name, Image, Likeness (NIL), or other Personal Data, in a engineered system. It pulls together best current practice from many sources including GDPR, the Article 29 Working Party, multiple ISO standards and the NIST RMF framework and presents it one place.

Newton’s Tree’s Federated AI Monitoring Service (FAMOS) is a dashboard for real-time monitoring of healthcare AI products. The dashboard is designed to enable users to observe and monitor the quality of data that goes into the AI, changes to the outputs of the AI, and developments in how healthcare staff use the product.

PLIM is designed to make benchmarking and continuous monitoring of LLMs safer and more fit for purpose. This is particularly important in high-risk environments, e.g. healthcare, finance, insurance and defence. Having community-based prompts to validate models as fit for purpose is safer in a world where LLMs are not static. 

AIxploit is a tool designed to evaluate and enhance the robustness of Large Language Models (LLMs) through adversarial testing. This tool simulates various attack scenarios to identify vulnerabilities and weaknesses in LLMs, ensuring they are more resilient and reliable in real-world applications.

Continuous proactive AI red teaming platform for AI and GenAI models, applications and agents.

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.

Continuous automated red teaming for AI, minimize security threats to AI models and applications.

Evaluating machine learning agents on machine learning engineering.