Cyber & IT Supervisory Forum - Additional Resources

Identify and establish acceptable resource consumption and efficiency, and GHG emissions levels, along with actions to be taken if indicators rise above acceptable levels. Estimate AI system emissions levels throughout the AI lifecycle via carbon calculators or similar process.

Organizations can document the following: Transparency & Documentation

Are greenhouse gas emissions, and energy and water consumption and efficiency tracked within the organization? Are deployed AI systems evaluated for potential upstream and downstream environmental impacts (e.g., increased consumption, increased emissions, etc.)? Could deployed AI systems cause environmental incidents, e.g., air or water pollution incidents, toxic spills, fires or explosions? GAO-21-519SP - Artificial Intelligence: An Accountability Framework for Federal Agencies & Other Entities. URL Artificial Intelligence Ethics Framework for the Intelligence Community. Datasheets for Datasets. URL AI Transparency Resources Organisation for Economic Co-operation and Development (OECD). "Measuring the environmental impacts of artificial intelligence compute and applications: The AI footprint.” OECD Digital Economy Papers, No. 341, OECD Publishing, Paris. Victor Schmidt, Alexandra Luccioni, Alexandre Lacoste, and Thomas Dandres. “Machine Learning CO2 Impact Calculator.” ML CO2 Impact, n.d. Alexandre Lacoste, Alexandra Luccioni, Victor Schmidt, and Thomas Dandres. "Quantifying the Carbon Emissions of Machine Learning." arXiv preprint, submitted November 4, 2019. Matthew Hutson. “Measuring AI’s Carbon Footprint: New Tools Track and Reduce Emissions from Machine Learning.” IEEE Spectrum, November 22, 2022. Association for Computing Machinery (ACM). "TechBriefs: Computing and Climate Change." ACM Technology Policy Council, November 2021. Roy Schwartz, Jesse Dodge, Noah A. Smith, and Oren Etzioni. “Green AI.” Communications of the ACM 63, no. 12 (December 2020): 54–63. References

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