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

The General Understanding on AI and Copyright, released by Japan Copyright Office aims at providing clarity on how Japan's current Copy Right Act should be applied in relation to AI technologies. Three main topics presented include: the training stage of AI developments, the generation, utilisation stage, and copyrightability of AI-generated material.

The AI Governance module available in TrustWorks suite helps to achieve regulatory compliance with the EU AI Act and the coming wave of AI regulations. The tool allows any privacy or AI governance leader to get instant visibility and control over the AI systems used within the organisation, with continuous risk monitoring, risk classification and fulfilment of transparency obligations.

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.

This Recommendation describes specifications of a data centre infrastructure management (DCIM) system based on big data and artificial intelligence (AI) technology.

Effective data management and operation is extremely important. This work item is purposed to draft a GR of data operation requirements and mechanisms to better serve ENI system.

The purpose of the present document is to provide information on software design principles for constructing modular systems to be applied to the ENI reference system architecture (and any other applicable ETSI reports or standards).

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.

An Artificial Intelligence Governance Risk and Assurance Platform for implementation guidance and assurance

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

The DVMS NIST Cybersecurity Framework Overlay System (DVMS NIST-CSF) provides organizations of any size, scale, or complexity an affordable way to mitigate cybersecurity risk to assure digital business performance, resilience & trust

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.

Based on the occurrence of specific events, the AIIA allows management and development teams to identify actual and potential impacts at the AI system level through a set of defined controls across stages of the system lifecycle.

Recommendation ITU-T F.748.13 defines the roles, technical and security requirements of a shared machine learning system, and provides technical architectures, functional components, and processing procedures of the shared machine learning system in the centralized and decentralized modes.

This Recommendation specifies use cases and requirements for multimedia communication enabled vehicle systems using artificial intelligence, including overview, use cases, high-layer architecture, service and network requirements, functional requirements, and non-functional requirements.

Code for our nips19 paper: You Only Propagate Once: Accelerating Adversarial Training Via Maximal Principle

YoloV3 Implemented in Tensorflow 2.0