Broadening the perspective for sustainable AI: Comprehensive sustainability criteria and indicators for AI systems

TL;DR

This paper introduces the SCAIS Framework, a comprehensive set of sustainability criteria and indicators for AI systems, aiming to promote a holistic and interdisciplinary approach to sustainable AI development and application.

Contribution

It presents a novel, interdisciplinary framework with 19 sustainability criteria and 67 indicators, based on expert input, to guide sustainable AI practices.

Findings

Developed 19 sustainability criteria for AI systems

Identified 67 indicators to measure sustainability

Provides a foundation for standards and tools in sustainable AI

Abstract

The increased use of AI systems is associated with multi-faceted societal, environmental, and economic consequences. These include non-transparent decision-making processes, discrimination, increasing inequalities, rising energy consumption and greenhouse gas emissions in AI model development and application, and an increasing concentration of economic power. By considering the multi-dimensionality of sustainability, this paper takes steps towards substantiating the call for an overarching perspective on "sustainable AI". It presents the SCAIS Framework (Sustainability Criteria and Indicators for Artificial Intelligence Systems) which contains a set 19 sustainability criteria for sustainable AI and 67 indicators that is based on the results of a critical review and expert workshops. This interdisciplinary approach contributes a unique holistic perspective to facilitate and structure the discourse on sustainable AI. Further, it provides a concrete framework that lays the foundation for developing standards and tools to support the conscious development and application of AI systems.