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.

Trustworthy AI Procurement CardTM is a non-exhaustive list of information which can accompany acquisition decisions. The Card is similar to the DataSheets or Model Cards, in the sense that the objective is to promote transparency and better due diligence during AI procurement process.

The deck of 50 Trustworthy AI Cards™ correspond to 50 most relevant concepts under the 5 categories of: Data, AI, Generative AI, Governance, and Society. The Cards are used to create awareness and literacy on opportunities and risks about AI, and how to govern these technologies.

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.

Warden AI provides independent, tech-led AI bias auditing, designed for both HR Tech platforms and enterprises deploying AI solutions in HR. As the adoption of AI in recruitment and HR processes grows, concerns around fairness have intensified. With the advent of regulations such as NYC Local Law 144 and the EU AI Act, organisations are under increasing pressure to demonstrate compliance and fairness.

Community jury is concept where multiple stakeholders impacted by a same technology are given the possibility to learn about a project, discuss with one another and provide feedback.

Evaluate input-output safeguards for LLM systems such as jailbreak and hallucination detectors, to understand how good they are and on which type of inputs they fail.

Responsible AI (RAI) Repairing Assistant

The Algorithm Charter for Aotearoa New Zealand is a set of voluntary commitments developed by Stats NZ in 2020 to increase public confidence and visibility around the use of algorithms within Aotearoa New Zealand’s public sector. In 2023, Stats NZ commissioned Simply Privacy to develop the Algorithm Impact Assessment Toolkit (AIA Toolkit) to help government agencies meet the Charter commitments. The AIA Toolkit is designed to facilitate informed decision-making about the benefits and risks of government use of algorithms.

BSI Flex 1890 defines terms, abbreviations, and acronyms for the connected and automated vehicles (CAVs) sector, focused on those relating to vehicles and associated technologies.

This DIN DKE SPEC defines guidelines for the labelling of training data for QA systems and specifies the characteristics of labels.

The purpose of the present document is to provide information on different types of AI mechanisms that can be used for cognitive networking and decision making in modern system design, including natural language processing.

This Recommendation presents an overview of the framework for a language learning system based on speech and natural language processing (NLP) technology.

This document establishes an Artificial Intelligence (AI) and Machine Learning (ML) framework for describing a generic AI system using ML technology.

This DIN SPEC (PAS) defines requirements for the development of deep learning image recognition systems. This document gives the conditions, under which image recognition problems can be processed with the help of a Deep-Learning-System.

This guide is published to create clarity for individuals involved with Software-Based Intelligent Process Automation products so that industry participants may rely on a product manufacturer's functionality claims and understand the underlying technological methods used to produce those functions.

The AI development interface, AI model interoperable representation, coding format, and model encapsulated format for efficient AI model inference, storage, distribution, and management are discussed in this standard.

This standard specifies an architecture and technical requirements for face recognition systems.

This standard provides a framework to help developers of autonomous systems both review and, if needed, design features into those systems to make them more transparent. The framework sets out requirements for those features, the transparency they bring to a system, and how they would be demonstrated in order to determine conformance with this standard.

This document surveys topics related to trustworthiness in AI systems.