A legally enforceable AI-user interaction framework that verifies informed consent through multimodal methods, protects user intellectual property via blockchain-based tracking, and ensures lifetime authorship rights with legal safeguards against unauthorized use or AI training reuse.

mobile Detection, Research, Education, Equipment, Training programme provides a mobile hearing loss detection test for children who are entering schooling (as well as for other life stages) and an AI-enabled application that turns a smartphone into a personalised hearing aid

ReadSpeaker is a SaaS-based text-to-speech platform providing natural-sounding, multilingual voices for seamless integration in web, document, and application environments.

aiD leverages artificial intelligence to improve communication accessibility for deaf and hard-of-hearing individuals through advanced speech-to-text and sign language technologies.

The Code sets out a process to identify, evaluate, and mitigate the known risks of AI to children and prepare for the known unknowns. It requires those who build and deploy AI systems to consider the foreseeable risks to children by design and default.

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.

PAS 1882:2021 details how information should be handled during automated vehicle trials to ensure it’s collected consistently and improves the safety of UK trials

This Recommendation describes the functional entities and architecture for emotion enabled multimodal user interface based on artificial neural networks.

This SRD deals with the ethical implications and moral questions that arise from the development and implementation of Artificial Intelligence (AI) technologies in the Active Assisted Living (AAL) context.

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

This Recommendation provides a framework for data handling to enable machine learning (ML) in future networks including International Mobile Telecommunications (IMT)-2020.

A blueprint for data usage and model building across organizations and devices while meeting applicable privacy, security and regulatory requirements is provided in this guide.

This standard defines a framework and architectures for machine learning in which a model is trained using encrypted data that has been aggregated from multiple sources and is processed by a third party trusted execution environment (TEE).

This document describes the history of biometrics and what biometrics does, the various biometric technologies in general use today (for example, fingerprint recognition and face recognition) and the architecture of the systems and the system processes that allow automated recognition using those technologies.

This document describes the history of biometrics and what biometrics does, the various biometric technologies in general use today.

This document builds upon the information provided in ISO/IEC TR 24714-1, ISO/IEC TR 29194 and ISO/IEC 29138-1 in order to highlight in a more detailed way the medical, physical and cognitive aspects that are specific for the use of biometrics by elderly persons.

This document establishes requirements for development of biometric solutions for verification and identification processes for secure access without physical contact with any device at any time.

This document provides an overarching data life cycle framework that is instantiable for any AI system from data ideation to decommission.

Privacy compliance platform, based on AI/Blockchain, which helps global companies to keep compliant with the data protection requirements.