Magazine #2 | Summer 2023
Step by Step Towards Sustainable AI
The sustainability of an AI system depends on many decisions taken during its lifecycle. At the SustAIn project, we have developed criteria that help us illustrate which steps should be heeded.
Phase 1: Planning and Design
To a significant degree, the sustainability of an AI system is determined during the design and planning stage. As such, careful consideration should be given from the start as to whether the development and deployment of a complex and resource-intensive AI system is even necessary for a particular task. If it is, then additional fundamental questions must be asked: What data must the system process and how? What are the risks? And what security measures will be implemented?
General Requirements Checklist
Social Requirements Checklist
Environmental Requirements Checklist
Economic Requirements Checklist
Phase 2: Data
Data is at the core of all machine learning-based AI systems. System sustainability depends on the quality and amount of data to be processed, its interpretation and the way in which the data is obtained.
General Requirements Checklist
Social Requirements Checklist
Environmental Requirements Checklist
Economic Requirements Checklist
Phase 3: Development
How is an AI model developed? Which model should be selected? How is the model trained? These questions have far-reaching implications, particularly for the social and environmental sustainability of AI systems. Energy consumption must be minimized during the development phase. Risks must also be contained.
General Requirements Checklist
Social Requirements Checklist
Environmental Requirements Checklist
Phase 4: Implementation
During the deployment and operation of AI systems, as well as during additional training runs, risks to data subjects must be monitored and steps taken to ensure data protection. During the application phase, the consumption of energy and resources must be kept low.