Resaro offers independent, third-party assurance of mission-critical AI systems. It promotes responsible, safe and robust AI adoption for enterprises, through technical advisory and evaluation of AI systems against emerging regulatory requirements.

Defining 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.

The document highlights quality objectives for organizations responsible for datasets. The document describes control of records during the lifecycle of datasets, including but not limited to data collection, annotation, transfer, utilization, storage, maintenance, updates, retirement, and other activities.

This document lists examples of and defines categories of use cases for machine learning in medicine for clinical practice.

This document gives the requirements, under which image recognition problems in medicine can be addressed using a Deep Learning image recognition system.

This standard identifies the core requirements and baseline for AI solutions in health care to be deemed as trustworthy.

This standard is aimed at establishing concepts and terminology for the performance and safety evaluation of artificial intelligence medical device, which covers basic technology, dataset, quality characteristics, quality evaluation and application scenario.

PRIDAR (Prioritization, Research, Innovation, Development, Analysis, and Review) A Risk Management Framework

Citrusx offers a multifaceted solution to connect all stakeholders in the company through an SDK, user-friendly UI, and automated reporting system.

This guidance addresses one type of generative AI, large multi-modal models (LMMs), which can accept one or more type of data input and generate diverse outputs that are not limited to the type of data fed into the algorithm.

Advice for policy-makers on how to plan, develop and implement federated learning systems to safely advance machine learning for health systems improvement.

Saidot is a platform that helps you create and manage AI applications that are safe, ethical and transparent. With Saidot, you can: 1) Adopt AI policies from a library of templates, 2) Identify, mitigate and monitor AI risks, 3) Share AI reports with your stakeholders and 4) Learn how to use generative AI responsibly.

Automate, simplify, and streamline your end-to-end AI risk management process.

Based on abstract interpretations, Saimple leverages state-of-the-art techniques described in ISO/IEC 24029 standards for the assessment and validation of AI model robustness

The Data Carbon Scorecard provides a rapid assessment of the environmental impact of your proposed data project enabling you to quickly gauge the CO2 implications of your data project and determine the ‘best’ course of action for the environment. Through answering just 9 questions, the scorecard provides you with a traffic light display of data CO2 hotspots and an overall Data CO2 Score for the whole project within minutes. The scorecard can be used by any individual, team, or organization.

QuantPi’s platform unites AI testing with AI governance. It is the cockpit of AI-first organizations to collaborate on – to efficiently and responsibly understand, enhance and steer their individual AI models as well as their complete AI landscape.

Aival software allows non-technical users to evaluate commercial AI products in terms of performance, fairness, robustness and explainability (healthcare / imaging)

GuArdIan: Essential safeguard for businesses utilizing ChatGPT-like LLM technology

Fast AI with Assurance, Integrity, and Reliability for comprehensive AI governance and oversight.

BetterBeliefs is an evidence-based and inclusive stakeholder engagement platform to create contextual, relevant and justified AI policy