This Recommendation specifies use cases and requirements for multimedia communication enabled vehicle systems using artificial intelligence, including overview, use cases, high-layer architecture, service and network requirements, functional requirements, and non-functional requirements.

An end-to-end model risk management platform that automates model documentation and dramatically simplifies AI model validation.

Teeny-Tiny Castle is a collection of tutorials on how to use tools for AI Ethics and Safety research.

Our work enables developers and policymakers to anticipate, measure, and address discrimination as language model capabilities and applications continue to expand.

Framework agnostic sliced/tiled inference + interactive ui + error analysis plots

A hyperparameter optimization framework

Relax! Flux is the ML library that doesn't make you tensor

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

Anomaly detection related books, papers, videos, and toolboxes

This is the First Edition of CAN/CIOSC 101:2019, Ethical design and use of automated decision systems. CAN/CIOSC 101:2019 was prepared by the CIO Strategy Council Technical Committee 2 (TC 2) on the ethical design and use of automated decision systems, comprised of over 100 thought leaders and experts in artificial intelligence, ethics, and related subjects.

In undertaking this voluntary commitment, developers and managers of advanced generative systems commit to working to achieve outcomes related to the OECD AI principles.

The Playbook provides suggested actions for achieving the outcomes laid out in the AI Risk Management Framework (AI RMF) Core (Tables 1 – 4 in AI RMF 1.0). Suggestions are aligned to each sub-category within the four AI RMF functions (Govern, Map, Measure, Manage).

The AIRC supports all AI actors in the development and deployment of trustworthy and responsible AI technologies. AIRC supports and operationalizes the NIST AI Risk Management Framework (AI RMF 1.0) and accompanying Playbook and will grow with enhancements to enable an interactive, role-based experience providing access to a wide-range of relevant AI resources.

The goal of the AI RMF is to offer a resource to the organizations designing, developing, deploying, or using AI systems to help manage the many risks of AI and promote trustworthy and responsible development and use of AI systems.

Monitaur is a model governance software company that enables you to build repeatable patterns and requirements for model development success. Those policies, templates, and applications are then also aligned with regulatory requirements. We're a full model development lifecycle governance solution, with interfaces specifically for development teams, and then also for risk management teams so that as a system of record, you gain transparency, alignment, and greater execution of success in your investments in AI.

Zero-trust 360 RAIAAS platform for audit and certification of enterprise-wide AI solutions using trust indicators following ethical standards

Calvin Risk develops comprehensive, quantitative solutions to assess and manage the risks of AI algorithms in commercial use. The tool helps companies create a framework for transparency, governance and standardization to manage their AI portfolios while ensuring that AI remains safe and compliant with the highest ethical standards and upcoming regulatory requirements.

Logically uses a Human in the Loop AI framework called HAMLET (Humans and Machines in the Loop Evaluation and Training) to enable the development of trustworthy and responsible AI technologies.

FAIRLY provides an AI governance platform focussed on accelerating the broad use of fair and responsible AI by helping organisations bring safer AI models to market.

Meta-learning is a promising strategy for learning to efficiently learn within new tasks, using data gathered from a distribution of tasks.