SECure: A Social and Environmental Certificate for AI Systems

Understand

In a world increasingly dominated by AI applications, an understudied aspect is the carbon and social footprint of these power-hungry algorithms that require copious computation and a trove of data for training and prediction.

• While profitable in the short-term, these practices are unsustainable and socially extractive from both a data-use and energy-use perspective.
• This work proposes an ESG-inspired framework combining socio-technical measures to build eco-socially responsible AI systems.
• The framework has four pillars: compute-efficient machine learning, federated learning, data sovereignty, and a LEEDesque certificate.