AI Screener to enable universal early screening for all children.

Dytective by Change Dyslexia is an innovative AI-powered tool designed to detect the risk of dyslexia in children quickly and reliably. Developed in collaboration with researchers, Dytective combines language exercises with machine learning to screen for dyslexia in just 15 minutes. Backed by scientific validation and used by schools and families worldwide, it empowers early intervention and promotes equal opportunities in education.

FloodMapp is a technology company that specialises in rapid real-time flood forecasting and flood inundation mapping to provide greater warning time and situational awareness.

Pano - Actionable Intelligence for Wildfire Management is an advanced, connected platform designed for fire professionals, enabling them to detect threats, verify fires, and share critical information with response teams faster than ever before.

SeismicAI is a provider of innovative Earthquake Early Warning Systems (EEW) ensuring earthquake preparedness. SeismicAI's algorithms utilise local sensors to issue high-precision alerts for earthquake preparedness. The system covers the full early warning cycle - from monitoring and reporting, through alerts, to optionally triggering automated preventive actions.

PetaBencana.id leverages AI to provide residents, government agencies, and first responders with a real-time disaster mapping platform for Indonesia.

Risk Atlas Nexus provides tooling to connect fragmented AI governance resources through a community-driven approach to curation of linkages between risks, datasets, benchmarks, and mitigations. It transforms abstract risk definitions into actionable AI governance workflows.

This tool provides a comprehensive risk management framework for frontier AI development, integrating established risk management principles with AI-specific practices. It combines four key components: risk identification through systematic methods, quantitative risk analysis, targeted risk treatment measures, and clear governance structures.

Behavior Elicitation Tool (BET) is a complex-AI system that systematically probes and elicits specific behaviors from cutting-edge LLMs. Whether for red-teaming or targeted behavioral analysis, this automated solution is Dynamic Optimized and Adversarial (DAO) and can be configured to test the robustness precisely and help to have a better control of the AI system.

COMPL-AI is an open-source compliance-centered evaluation framework for Generative AI models

This document addresses how artificial intelligence machine learning can impact the safety of machinery and machinery systems.

AIxploit is a tool designed to evaluate and enhance the robustness of Large Language Models (LLMs) through adversarial testing. This tool simulates various attack scenarios to identify vulnerabilities and weaknesses in LLMs, ensuring they are more resilient and reliable in real-world applications.

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

garak, Generative AI Red-teaming & Assessment Kit, is an LLM vulnerability scanner. Garak checks if an LLM can be made to fail.

A fast, scalable, and open-source framework for evaluating automated red teaming methods and LLM attacks/defenses. HarmBench has out-of-the-box support for transformers-compatible LLMs, numerous closed-source APIs, and several multimodal models.

Harms Modeling is a practice designed to help you anticipate the potential for harm, identify gaps in product that could put people at risk, and ultimately create approaches that proactively address harm.

Dioptra is an open source software test platform for assessing the trustworthy characteristics of artificial intelligence (AI). It helps developers on determining which types of attacks may impact negatively their model's performance.

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.

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 DIN SPEC series describes a number of AI quality requirements which are structured using an AI quality meta model. The DIN SPEC series applies to all phases of the life cycle of an AI module.