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Are we assessing our AI system’s environmental impact across its entire life cycle?
Are we assessing our AI system’s environmental impact across its entire life cycle?
An AI system’s environmental footprint goes beyond its operational phase. A full life cycle assessment (LCA) should account for resource extraction, hardware manufacturing, training, deployment, and end-of-life disposal. Key impact indicators include CO2 emissions, energy and water consumption, and raw material use. Since many AI systems run in mixed-use facilities, properly allocating environmental costs can be complex but necessary for accurate reporting.
If you answered No then you are at risk
If you are not sure, then you might be at risk too
Recommendations
- Analyze the full environmental footprint of your system, from development to retirement.
- Use clear metrics (e.g., emissions per token or annual energy use) to monitor impact.
- Develop methodologies to fairly allocate environmental costs in shared computing environments.
- Integrate LCA results into corporate reporting and sustainability strategies.