As artificial intelligence (AI) technologies continue to expand rapidly across industries and society, concerns about their environmental footprint are intensifying. While the potential benefits of AI for economic growth, innovation, and climate solutions are significant, there remain crucial gaps in how AI’s environmental impact — including energy consumption, water use, greenhouse gas emissions, and electronic waste — is measured and managed. The pioneering ITU report “Measuring what matters: How to assess AI’s environmental impact” identified critical challenges such as reliance on indirect estimates, fragmented data across AI lifecycle stages, and a lack of standardized, transparent frameworks for reporting and accountability.
Why we were at AI for Good: grounding artificial intelligence...
Ask anyone building or using AI tools for research what their biggest concerns are, and hallucinations and citation problems come...
AI for Good | Global Summit
From open data to local impact: An MCP server and...
Imagine you are a climate researcher studying drought patterns across East Africa. The satellite data exists, hosted free on AWS,...
AI for Good | Global Summit
Building the infrastructure for more equitable access to AI
As AI moves into everyday services, the infrastructure beneath it is becoming increasingly important. Connectivity, computing capacity, energy efficiency, security,...
AI for Good | Global Summit
