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.

Type

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Fairness

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Origin

Scope

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Approach Technical
Objective Fairness

TechnicalInternationalUploaded on Apr 23, 2024
Based on an extensive criteria catalogue, the Digital Trust Label is awarded to trustworthy digital services after an audit

Objective(s)

Related lifecycle stage(s)

Operate & monitorDeployPlan & design

TechnicalUploaded on Apr 22, 2024
A clear, concise, simple yet powerful and efficient API for deep learning.

Objective(s)

Related lifecycle stage(s)

Build & interpret model

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.

Objective(s)

Related lifecycle stage(s)

Build & interpret modelPlan & design

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

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

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

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

Objective(s)

Related lifecycle stage(s)

Build & interpret model

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

TechnicalUnited StatesUploaded on Apr 22, 2024
XLNet: Generalized Autoregressive Pretraining for Language Understanding

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Build & interpret modelPlan & design

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

Objective(s)

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Build & interpret model

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

Objective(s)

Related lifecycle stage(s)

Build & interpret model

TechnicalSwitzerlandUploaded on Apr 22, 2024<1 day
Our community’s free course in a Human Rights-based approach to AI development explores how we can concretely build systems centering these values and is enriched by reading and discussion groups.

TechnicalUnited 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.

Objective(s)

Related lifecycle stage(s)

Plan & design

TechnicalIsraelUploaded on Apr 11, 2024
Citrusx offers a multifaceted solution to connect all stakeholders in the company through an SDK, user-friendly UI, and automated reporting system.

TechnicalUploaded on Apr 3, 2024
Machine Learning for Wildlife Image Classification

Objective(s)


TechnicalUnited StatesUploaded on Apr 2, 2024
:satellite: Simple and ready-to-use tutorials for TensorFlow

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Related lifecycle stage(s)

Collect & process data

TechnicalUnited StatesUploaded on Apr 2, 2024
TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)

Objective(s)

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Build & interpret modelPlan & design

TechnicalUploaded on Apr 2, 2024
A Neural Net Training Interface on TensorFlow, with focus on speed + flexibility

Objective(s)

Related lifecycle stage(s)

Build & interpret model

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

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Related lifecycle stage(s)

Build & interpret modelPlan & design

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

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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.