Nicholas Correa

Abstract Here we quantify the material footprint of artificial intelligence training by linking computational workloads to physical hardware needs. Graphics processing unit demand is expressed as equivalent hardware lifetime consumed and, thus, the material demand involved. As computational demands rise, assessing the environmental impact of artificial intelligence requires moving beyond energy and water consumption to include the material demands of specialized hardware.

• Accountability/liability: accountability refers to the idea that developers and deployers of AI technologies should be compliant with regulatory bodies. These actors should also be accountable for their actions and the impacts caused by their technologies. • Beneficence/non-maleficence: beneficence and non-maleficence come from bioethics and medical ethics. In AI ethics, these principles state that human welfare (and harm aversion) should be the goal of AI-empowered technologies.