This guidance document is intended to support machine learning (ML) researchers and operators to measure and improve the environmental efficiency of ML, artificial intelligence (AI) and other emerging technologies use in supply chain management.

This standard specifies a framework for adding artificial intelligence (AI) functions to support the energy management agent (EMA) specified in ISO/IEC for EMAs located on customer premises.

This Recommendation establishes the concept of artificial intelligence service exposure (AISE) for smart sustainable cities (SSCs).

Recommendation ITU-T M.3381 provides requirements for energy saving management of a 5G radio access network (RAN) system with artificial intelligence (AI).

This Supplement aims to investigate appropriate models to evaluate urban energy efficiency with a special focus on the emerging adoption of AI and big data.

Panel to develop good practice in the use of new technologies like AI in the planning of the major infrastructure that are critical for the delivery of national goals such as net zero, resilience and nature recovery.

LLM Survey for responsible and transparent and safe AI covering international compliance regulations and data/ model evaluations

Advice for government, regulators and developers on how to minimize environmental impacts across the full life cycle of large language models.

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 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.

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.

The Trustworthy and Ethical Assurance platform is an open-source tool and framework to support the process of developing and communicating trustworthy and ethical assurance cases for data-driven technologies.

Using the latest advances in ethical artificial intelligence, CESIUM supports risk-assessment for safeguarding the most vulnerable children in society.

The Data Carbon Scorecard provides a rapid assessment of the environmental impact of your proposed data project enabling you to quickly gauge the CO2 implications of your data project and determine the ‘best’ course of action for the environment. Through answering just 9 questions, the scorecard provides you with a traffic light display of data CO2 hotspots and an overall Data CO2 Score for the whole project within minutes. The scorecard can be used by any individual, team, or organization.

Generative AI presents unprecedented opportunities. But it also forces CEOs to grapple with towering unknowns, and to do so in a space that may feel unfamiliar or uncomfortable. Crafting an effective strategic approach to generative AI can help distinguish the signal from the noise. Leaders who are prepared to reimagine their business models—identifying the right opportunities, organizing their workforce and operating models to support generative AI innovation, and ensuring that experimentation doesn’t come at the expense of security and ethics—can create long-term competitive advantage.

Salesforce is embedding ethical guardrails and guidance across their products to help customers innovate responsibly — and catch potential problems before they happen. Given the tremendous opportunities and challenges emerging in this space, they are building on their Trusted AI Principles with a new set of guidelines focused on the responsible development and implementation of generative AI.

The experiment-impact-tracker is meant to be a simple drop-in method to track energy usage, carbon emissions, and compute utilization of your system.

Carbon estimation of LLMs.

Track and reduce CO2 emissions from your computing