Metric Hub is an online platform developed by PTB and partners for assessing the quality of data used in medical AI. Its metric library of 60 data quality metrics puts into practice the METRIC-framework, and each metric comes with a standardised card describing its definition, applicability and pitfalls. Decision trees guide users to the metrics that suit their use case. This enables systematic, fit-for-purpose evaluation of training and test data, supporting trustworthy AI and regulatory compliance.

PrivFair is a library for auditing the fairness of machine learning models while protecting privacy. Fairness audits require sensitive data about attributes such as gender or race, which is protected by law. PrivFair uses Secure Multiparty Computation, a cryptographic technique, so that an external auditor can test a company's model without either party revealing their data or model. It supports group fairness audits on tabular and image data. This enables independent, privacy-preserving checks of proprietary AI models.

IEEE 3198-2025 is a technical standard that specifies a method for evaluating the fairness of machine learning systems. It was published by the IEEE Standards Association in May 2025. The standard categorises the causes of unfairness in machine learning and presents widely used definitions of fairness. For each definition, it specifies metrics and explains how to calculate them. Test cases set out detailed procedures for fairness evaluations. This supports consistent, repeatable fairness testing across systems and organisations.

Google's Differential Privacy libraries are an open-source collection of tools for producing statistics from sensitive data while protecting individuals' privacy. They add calibrated random noise to results such as counts and averages, so no single person can be identified. Building block libraries are available in C++, Go and Java. End-to-end frameworks make the tools usable by non-experts on large-scale data systems. Further tools track privacy budgets and audit privacy guarantees. The libraries are released under the Apache 2.0 licence.

Audit the Judge is an open-source tool for checking whether large language models used as judges evaluate answers reliably. It builds answer pairs where the correct verdict is known. These test whether the judge invents preferences between equal answers or favours answers shown first or written at greater length. Results are presented in a one-page report with statistical confidence intervals. The tool works with any major LLM judge. It is written in Python and released under the MIT licence.

ASR-FairBench is an online leaderboard that ranks speech recognition systems on both accuracy and fairness. It was developed by researchers at IIT Kharagpur. The leaderboard tests models on recordings from speakers of different ages, genders, ethnicities and backgrounds. It measures how error rates differ across these groups and combines this with overall accuracy into a single score. Models are rated from "severely biased" to "exemplarily fair". Users can submit their own models and receive a fairness audit within minutes.

CEB (Compositional Evaluation Benchmark for Fairness) is a fairness evaluation benchmark containing about 11 000 samples spanning multiple bias types, social groups, and tasks. It enables consistent measurement of compositional fairness behavior to identify failures that occur when fairness conditions combine across groups and bias factors. It is used by developers and auditors to support the fairness and accountability objectives, and to assess robustness of fairness performance under varied evaluation settings.

LangFair is a Python library for conducting use-case level LLM bias and fairness assessments

VerifyWise is an open-source AI governance and evaluation tool designed to help organisations manage AI compliance, risk, and assurance activities. It provides capabilities for AI system inventories, risk assessments, policy management, governance workflows, compliance monitoring, and LLM evaluations, with support for major frameworks and regulations, including the EU AI Act, ISO/IEC 42001, and the NIST AI Risk Management Framework. The platform can be self-hosted and is designed to support evidence-based governance, audit readiness, and continuous monitoring of AI systems.

SB-Bench is an open source benchmark that evaluates stereotype bias in Large Multimodal Models, using 7 500 real world images across nine social bias categories. It tests whether models default to stereotypical assumptions in ambiguous scenarios and finds that adding vision capabilities to language models consistently increases bias compared with text only baselines. The benchmark, code and dataset are publicly available, offering developers and auditors a reproducible tool for assessing fairness in multimodal AI systems.

RecourseBench is a modular evaluation framework for algorithmic recourse methods that emphasises 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.

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...

An open-source Python library designed for developers to calculate fairness metrics and assess bias in machine learning models. This library provides a comprehensive set of tools to ensure transparency, accountability, and ethical AI development.

This bias detection tool identifies potentially unfairly treated groups of similar users by a binary algorithmic classifier. The tool identifies clusters of users that face a higher misclassification rate compared to the rest of the data set. Clustering is an unsupervised ML method, so no data is needed is required on protected attributes of users. The metric by which bias is defined can be manually chosen in advance: False Negative Rate (FNR), False Positive Rate (FPR), or Accuracy (Acc).

Biaslyze is a python package that helps to get started with the analysis of bias within NLP models and offers a concrete entry point for further impact assessments and mitigation measures.

PAM is the contextual bias detection solution for AI and ML models. Achieve trustworthiness by identifying hidden bias prior to launching and improving explainability. Get a socio-technical contextual view in real-time in your production environment to ensure compliance with AI regulations.

This repository contains a sample implementation of Gradient Feature Auditing (GFA) meant to be generalizable to most datasets.

CounterGen is a framework for auditing and reducing bias in natural language processing (NLP) models such as generative models (e.g. ChatGPT, GPT-J, GPT-3 etc.) or classification models (e.g. BERT). It does so by generating balanced datasets, evaluating the behavior of NLP models, and directly editing the internals of the model to reduce bias.

Tool that uses statistical analysis to identify groups that may be subject to unfair treatment by an algorithmic-driven decision-support system. The tool informs the qualitative doctrine of law and ethics which disparities need to be scrutinised manually by human experts.