Researchers propose framework to measure AI's social and environmental impact

In a newly published paper on the preprint server Arxiv.org, Through techniques like compute-efficient machine learning, federated learning, and data sovereignty, the coauthors assert scientists and practitioners have the power to cut contributions to the carbon footprint while restoring trust in historically opaque systems. Sustainability, privacy, and transparency remain underaddressed and unsolved challenges in AI. In June 2019, researchers at the University of Massachusetts at Amherst released a study estimating that the amount of power required for training and searching a given model involves the emission of roughly 626,000 pounds of carbon dioxide -- equivalent to nearly 5 times the lifetime emissions of the average U.S. car. Partnerships like those pursued by DeepMind and the U.K.'s National Health Service conceal the true nature of AI systems being developed and piloted.