FLARE-AI is an open-source AI flaw and incident reporting platform that lets anyone document a flaw in any AI system and route a single, standardised report to multiple developers and coordinators at once. It enables any AI actor to document vulnerabilities, biases, or incidents and route a single, standardised (JSON-LD) report to multiple developers, coordinators, and registries in the ecosystem.

RecourseBench is a modular evaluation framework for algorithmic recourse methods that emphasizes reproducibility when assessing user-facing counterfactual explanations. It enables practitioners to systematically compare recourse methods based on how they support actionable changes in decision-making systems. It is intended for researchers and developers who validate and improve the trustworthiness of explanation and human-agency features in AI used for consequential decisions.

Toolkit for evaluating fairness and bias in machine learning models using multiple subgroup fairness metrics (including parity and equalized-odds-style measures). It supports fairness auditing by quantifying disparities across demographic or other defined subgroups. Data scientists and developers can use it to verify and validate fairness properties and to guide improvements toward fairer model behavior.

The REFRAIME Legal Toolkit provides legal practitioners, public authorities, and civil society organisations with a structured resource for identifying and addressing the impact of AI systems on fundamental rights under the EU Charter. Developed by a consortium including the Center for the Study of Democracy, the European Center for Not-for-Profit Law, and the University of Malta, and co-funded by the European Union, the toolkit combines knowledge articles, sixteen real-world case studies grounded in actual case law (including ACLU v. Clearview AI, SCHUFA before the CJEU, and the Dutch Childcare Benefits case), an interactive glossary, a curated directory of EU, Council of Europe, UN, and OECD instruments, and a 43-point checklist for monitoring compliance with Fundamental Rights Impact Assessment obligations under Article 27 of the EU AI Act.

The AIM Framework (Awareness, Identification, Mitigation) presents a stepwise approach for the implementation of risk management strategies. The framework is intended for AI developers working in private, academic, or public sectors. It features a checklist with indicative scenarios for awareness-raising and training purposes.

A conformance corpus and reference verifier for execution evidence about AI agents. Each vector is a signed attestation with an expected verdict, so an implementer can run someone else's verifier against the corpus and find out whether it accepts what it must accept and refuses what it must refuse. The corpus includes adversarial cases that a permissive verifier passes and a correct one rejects. It runs offline and requires no network access or account. Apache-2.0.

SafeIMG is a safety-oriented benchmark for evaluating synthetic-image detection and visual-evidence verification in high-risk public- and personal-safety scenarios. It provides scenario-specific synthetic images generated for assessing how misleading visuals could impact safety and accountability. Researchers and developers can use it to verify and compare image authenticity methods, and to improve robustness of safeguards for trustworthy visual content.

ModelBench is a benchmarking tool for running safety-focused evaluations on AI models and producing detailed reports on performance against the benchmark suite. It is intended for developers and researchers who need to verify and compare safety/robustness behavior before deployment and during ongoing evaluation. The outputs support auditing and model validation for safety-related trustworthiness objectives.

OpenART is an open-source framework designed to evaluate the security of AI agents in dynamic, long-horizon, and stateful environments. It stress-tests agent runtimes against multi-step state poisoning, privilege escalation, and tool-use vulnerabilities across more than 10,000 benchmark scenarios.

IndicSafeEval is a multilingual benchmark framework for evaluating the safety and robustness of large language models (LLMs) against persuasion-based jailbreak attacks in Indian languages. It combines safety-critical content categories with multiple human-like persuasion strategies and evaluates model responses across several languages. The framework is designed to identify safety and alignment failures in non-English settings and can be used to assess and compare model behavior, support model validation, and monitor safety performance.

Legalithm is a free, open-source developer toolkit that brings EU AI Act compliance directly into the software development workflow. It classifies an AI system's risk tier under Regulation (EU) 2024/1689 and generates a dated, auditable compliance record. It also supports Article 50(2) transparency duties by watermarking AI-generated content (via C2PA credentials and pixel watermarking) and verifying such marks. A GitHub Action integration fails continuous integration builds when the compliance record drifts from the codebase or from regulatory deadlines, helping engineering teams catch compliance gaps early.

CXO Ready is a commercial SaaS platform that helps organisations inventory their AI systems, score each one against the EU AI Act, UK GDPR, and ISO 42001, and generate a prioritised, evidence-backed action plan.

explainX/explainx is a Python toolkit for generating explanations and debugging insights for black-box machine learning models. It produces explanation outputs for model predictions or behavior, suppo...

Robust and Reliable Algorithmic Recourse (ROAR) is a framework for generating instance-level algorithmic recourse that is designed to remain reliable when the underlying predictive model changes. It helps practitioners evaluate and improve the robustness of recourse/decision-support explanations so that suggested actions continue to work under model or distribution shifts. Target users include researchers and developers working on fair/robust recourse systems to address robustness and accountability-related trustworthiness objectives.

The AI Slop Side Effect Database documents indirect harms to legitimate users, creators, researchers, and organisations caused by the proliferation of low-quality AI-generated content and by countermeasures introduced to control it. It classifies cases across gatekeeping failures, content contamination, discriminatory bias, institutional invisibility, and service self-contamination, with evidence levels, affected parties, sources, and analytical commentary.

Ster is an open-source framework for intervening in large language model behavior at the level of internal activations to reduce harmful outputs and hallucinations. It helps users improve safety and robustness by enabling targeted controls that affect what the model produces, supporting transparency by making activation-level intervention mechanisms available for inspection and explanation.

TrustyAI Explainability Toolkit is a software toolkit for generating, transforming, and managing explanations of AI model behavior. It helps practitioners produce explanation artifacts that support the trustworthiness objectives of explainability and transparency, enabling users to inspect and communicate how model outputs are derived.

Human Approval Gate is a free, platform-neutral educational kit that helps leaders, educators, operators, and small teams define what a qualified person must check before AI-assisted work can affect a real decision or action. It includes a practical guide, printable worksheet, facilitator notes, and ten synthetic test cases. Its CLEAR test holds the consequence, names the reviewer and evidence, preserves accept, revise, reject, and escalate outcomes, and records the decision and recovery path.

The Green Algorithms calculator is an open-access online tool designed to estimate and report the carbon footprint of computational tasks and AI models. Developed by researchers at the University of Cambridge, the initiative addresses the growing, yet often overlooked, environmental impact of modern computing, ranging from high-performance scientific simulations to AI models and big data analytics. It can be used during the planning phase to estimate environmental impacts, or retrospectively for accounting and monitoring.

An open specification and command line interface (CLI) toolkit for cryptographically pre-registering machine learning evaluation criteria, including the metric, threshold, dataset, and seed, before a model is run. By committing these criteria in advance, the tool makes any post hoc changes to success thresholds detectable rather than silent, supporting greater integrity and accountability in reported ML evaluation claims.