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

To accelerate the journey to net zero, our team has designed the first publicly available tool to be used on new data projects to assess and measure the data CO2 footprint along the dataflow, called the “Data Carbon Ladder”. Using this tool, organizations can reduce their data CO2 footprint by ensuring optimal dataset(s) selection, frequency of updates, storage location, and analytics for the project to not only succeed but to do so in the most environmentally friendly way.

An AI tool that allows you to understand complex civic issues by listening to the perceptions and concerns of millions of citizens in Latin America and the Caribbean in real time.

GRACE is an AI governance platform that offers a central registry for all AI models, tools, and workflows for risk mitigation, compliance, and collaboration across AI teams and projects. It provides real-time compliance, transparency, and trust while promoting AI innovation. The platform works across all major cloud vendors and offers out-of-the-box frameworks for complying with EU AI Act, AI standards, data regulations, and AI frameworks.

The Human-Ai Paradigm for Ethics, Conduct and Risk (HAiPECR)

Framework for meaningful engagement of external stakeholders has been developed in the context of AI impact assessments. The Framework articulates minimum criteria for engagement and uses these as a baseline for involving companies, public entities as well as multilateral bodies during AI design and development. It also includes practical tools, steps and phases building on existing work on human rights due diligence.

Climate change, infectious diseases, disasters, air pollution, migration and poverty are among the most critical challenges the world faces today. This four-week, online course looks at the role mobile technology, advanced analytics and artificial intelligence can play in drastically improving the effectiveness and efficiency of planning and responses to these challenges.