Participatory Harm Auditing Workbenches and Methodologies (PHAWM) is a guiding framework and online tool for participatory AI auditing. It enables people without a technical AI background to audit the harms of AI applications. The methodology guides organisations through planning and completing an audit. The workbench takes auditors through four phases: understand, define, evaluate and recommend. Audit results inform decisions on whether to adopt AI. PHAWM was co-designed with end-users and affected people, and is free to use.

AI+ Certification is a certification scheme for AI products and services operated by the Korean Standards Association (KSA). It combines two assessments. An on-site audit, based on ISO/IEC 42001, checks how an organisation manages AI risks and improves quality over time. Product testing by Wisestone, based on ISO/IEC 25059 and 25051, evaluates product quality and AI functions. Certification helps organisations show that their AI products are reliable. It also builds trust with customers and supports alignment with international standards.

The Deployer AI Risk Register (DARR) is an open catalogue of 82 AI risks and 61 security sub-risks for organisations deploying AI systems. It consolidates the MIT AI Risk Repository with ISO/IEC standards, MITRE ATLAS and the EU AI Act, and groups risks into seven families aligned with existing enterprise risk functions. Each risk has a stable identifier and mappings to external frameworks. The data is freely available in CSV and JSON under a CC BY 4.0 licence.

MISSION KI is developing a voluntary quality standard guideline for artificial intelligence (AI) that strengthens the reliability and trustworthiness of AI applications and systems. It sets a voluntary, evidence-based self-assessment framework for AI providers below the EU AI Act’s high-risk threshold. It defines six quality dimensions (data governance, non-discrimination, transparency, human oversight, reliability, AI-specific cybersecurity) and a stepwise procedure: describe the use case, analyse protection needs, rate requirements via a VCIO catalogue, document tests/evidence, validate findings, issue a report, and monitor validity.

MISSION KI's Compliance Monitor is a tool for monitoring compliance with legal frameworks to facilitate interoperability, data flows and benefit sharing.

ADMIT is a research tool within a broader methodological framework combining quantitative and qualitative strategies to identify, analyse, and mitigate social implications associated with automated decision-making systems while enhancing their potential benefits. It supports comprehensive assessments of sociotechnical impacts to inform responsible design, deployment, and governance of automation technologies.

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.

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

This is an interactive bot that offers advice on complying with existing and intended AI regulations in various jurisdictions and offers a general risk analysis of the selected technology.

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

The Algorithmic Transparency Recording Standard (ATRS) is a framework for capturing information about algorithmic tools, including AI systems. It is designed to help public sector bodies openly publish information about the algorithmic tools they use in decision-making processes that affect members of the public.

Diagnose bias in LLMs (Large Language Models) from various points of views, allowing users to choose the most appropriate LLM.

AI Risk Assessment Tool for responsible, transparent and safe AI covering international compliance regulations and data / model evaluations

Ethical Problem Solving (EPS) is a framework to promote the development of safe and ethical artificial intelligence. EPS is divided into an evaluation stage (performed via Algorithmic Impact Assessment tools) and a recommendation stage (the WHY-SHOULD-HOW method).

Anekanta® AI’s AI Risk Intelligence System™ solves the problem of biometrics and AI system risk classification by challenging the entire AI system lifecycle and providing risk reports which inform compliance requirements in line with the EU AI Act and international regulatory frameworks.

How can we ensure a trustworthy use of automated decision-making systems (ADMS) in the public administration? AlgorithmWatch has developed a concrete and practicable impact assessment tool for ADMS in the public sector. This publication provides a framework ready to be implemented for the evaluation of specific ADMS by public authorities at different levels.

Human rights impact assessments of AI systems require meaningful engagement of individuals and groups most affected.It is particularly relevant for products and services using AI because there is still not enough practical guidance on how to involve stakeholders in the design, development and deployment of AI systems.

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

Fujitsu AI Ethics Impact Assessment assesses the potential risks and unwanted consequences of an AI system throughout its lifecycle and produces evidence which can be used to engage with auditors, approvers, and stakeholders. This is a process-driven technology that allows to: 1) map all interactions among the stakeholders and the components of the AI system; 2) assess the ethical risks emerging from such interactions; 3) understand the mechanisms whereby incidental events could occur, based on previous AI ethics incidents.