Catalogue of Tools & Metrics for Trustworthy AI

These tools and metrics are designed to help AI actors develop and use trustworthy AI systems and applications that respect human rights and are fair, transparent, explainable, robust, secure and safe.

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Respect of human rights

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Objective Respect of human rights

TechnicalUnited StatesUploaded on Apr 22, 2024
Data platform for LLMs - Load, index, retrieve and sync any unstructured data

Related lifecycle stage(s)

Build & interpret model

TechnicalUnited StatesUploaded on Apr 22, 2024
State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and performance on enterprise-grade infrastructure.

Related lifecycle stage(s)

Build & interpret model

TechnicalUploaded on Apr 22, 2024
A flexible framework of neural networks for deep learning

Related lifecycle stage(s)

Build & interpret model

TechnicalUnited KingdomUploaded on Apr 22, 2024
Tutorials, assignments, and competitions for MIT Deep Learning related courses.

TechnicalFinlandUploaded on Apr 22, 2024
Jupyter notebooks for teaching/learning Python 3

Related lifecycle stage(s)

Build & interpret model

TechnicalUnited StatesUploaded on Apr 22, 2024
Turi Create simplifies the development of custom machine learning models.

TechnicalSpainUploaded on Apr 22, 2024
Classical equations and diagrams in machine learning

TechnicalGreeceUploaded on Apr 22, 2024
Python Audio Analysis Library: Feature Extraction, Classification, Segmentation and Applications

TechnicalUnited StatesUploaded on Apr 22, 2024
NVIDIA® TensorRT™, an SDK for high-performance deep learning inference, includes a deep learning inference optimizer and runtime that delivers low latency and high throughput for inference applications.

Related lifecycle stage(s)

Build & interpret modelPlan & design

TechnicalProceduralUnited StatesJapanUploaded on Apr 19, 2024
Diagnose bias in LLMs (Large Language Models) from various points of views, allowing users to choose the most appropriate LLM.

Related lifecycle stage(s)

Plan & design

TechnicalUploaded on Apr 2, 2024
A Python package for identifying 42 kinds of animals, training custom models, and estimating distance from camera trap videos

Related lifecycle stage(s)

Build & interpret model

TechnicalUnited StatesUploaded on Apr 2, 2024
Code for our nips19 paper: You Only Propagate Once: Accelerating Adversarial Training Via Maximal Principle

Related lifecycle stage(s)

Build & interpret model

TechnicalUnited StatesUploaded on Apr 2, 2024
YoloV3 Implemented in Tensorflow 2.0

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Build & interpret model

TechnicalUploaded on Apr 2, 2024
The open big data serving engine. https://vespa.ai

TechnicalUnited StatesUploaded on Apr 2, 2024
NEW - YOLOv8 🚀 in PyTorch > ONNX > OpenVINO > CoreML > TFLite

TechnicalUploaded on Apr 2, 2024
trustworthy AI related projects

TechnicalUploaded on Apr 2, 2024
A repository to quickly generate synthetic data and associated trojaned deep learning models

Related lifecycle stage(s)

Build & interpret model

TechnicalFranceUploaded on Apr 2, 2024
Class activation maps for your PyTorch models (CAM, Grad-CAM, Grad-CAM++, Smooth Grad-CAM++, Score-CAM, SS-CAM, IS-CAM, XGrad-CAM, Layer-CAM)

Related lifecycle stage(s)

Build & interpret model

TechnicalUploaded on Apr 2, 2024
Deep learning library featuring a higher-level API for TensorFlow.

TechnicalUnited StatesUploaded on Apr 2, 2024
Debugging, monitoring and visualization for Python Machine Learning and Data Science

Related lifecycle stage(s)

Build & interpret modelPlan & design

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Disclaimer: The tools and metrics featured herein are solely those of the originating authors and are not vetted or endorsed by the OECD or its member countries. The Organisation cannot be held responsible for possible issues resulting from the posting of links to third parties' tools and metrics on this catalogue. More on the methodology can be found at https://oecd.ai/catalogue/faq.