Aanpak Begeleidingsethiek: A facilitated workshop method in which the people affected by a new technology map its effects and values together and agree on concrete actions for the technology, its setting and its users.
AI Fairness 360: An open-source Python and R toolkit for measuring bias in datasets and models and for trying out algorithms that reduce it before, during or after training.
AI Impact Assessment: A question set that walks a multidisciplinary team through the purpose, impact, technical design, data and risks of an AI system, and records the decision whether to use it.
Algoritmekader: A Dutch government website that brings together the legal requirements, recommended measures and supporting tools for responsible use of algorithms and AI, organised by lifecycle phase, role and topic.
ALTAI: A self-assessment checklist from the EU High-Level Expert Group on AI that helps you test an AI system against the seven requirements for trustworthy AI and spot where you need extra measures.
ARX: Free desktop software and Java library for anonymising structured personal data, with built-in checks of re-identification risk and of how much useful information remains.
CodeCarbon: An open-source Python package that estimates the electricity use and carbon emissions of your code while it runs, for measuring and reporting the footprint of training and running models.
Data Ethics Canvas: A one-page canvas of questions that helps you spot and manage the ethical issues of a project that collects, shares or uses data, at the start and as the project evolves.
Datasheets for Datasets: A set of questions you answer about a dataset, from why it was made to how it is maintained, so others can judge whether it suits their purpose.
DECIDE-AI: A reporting checklist for small, early clinical studies of AI decision support, where you first test a tool with real users before a large trial.
DEDA: A poster and handbook that walk a project team through the ethical and practical questions of a data or AI project and help you document the choices you make.
Evidently: An open-source Python library for checking data quality, data drift and model performance, which you can run as one-off reports or as an ongoing monitoring dashboard.
Fairlearn: An open-source Python library that breaks model performance down by subgroup and offers algorithms to reduce the gaps you find.
Flower: An open-source Python framework for federated learning: you train one model across several sites while patient data stays where it is.
IAMA: A structured discussion guide that helps a public organisation decide whether and how to use an algorithm, with a step-by-step check of its effects on fundamental rights.
MLflow: An open-source platform that records what went into each model run and keeps a versioned registry of trained models, which lets you trace any deployed model back to its code, data and settings.
Model Cards: A short, structured document that travels with a trained model and tells readers what it is for, how it was tested and where it performs worse.
Moral Medtech: A cooperative serious game in which patients, clinicians, developers and regulators agree which values a medical AI tool must protect and turn them into design requirements.
PROBAST+AI: A structured tool for judging the quality, risk of bias and applicability of studies that develop or test a clinical prediction model, including AI models.
SHAP: A Python library that explains individual predictions by estimating how much each input feature pushed the model’s output up or down, based on Shapley values from game theory.
System Usability Scale: A ten-item questionnaire that produces a single score from 0 to 100 for how usable people find a system.
TRIPOD+AI: A 27-item checklist for writing up studies that develop or validate a clinical prediction model, whether it uses regression or machine learning.
Value Sensitive Design: A design approach, with ready-made methods and card decks, for identifying the people and human values a technology affects and building those values into the design from the start.