Artificial intelligence for carbon emissions using system of systems theory

The impact of artificial intelligence (AI) on the environment is the subject of discourse, with arguments for both positive and negative effects. There is a fine line between AI for good and AI for environmental degradation. Today, companies want to seize the benefits of AI, which distinctively involves reducing the company's carbon footprint. However, AI's carbon emissions differ as per the techniques involved in training it. As the saying goes, a coin always has two sides. Therefore, it cannot be denied that AI can be an effective tool for combating climate change, but its role in contributing to carbon emissions cannot be ignored. Multiple studies indicate that AI could be the game-changer in staving off anthropogenic climatic changes due to the deterioration of the environment and global warming. This double-edged relationship and interdependency of AI and carbon emissions are represented through a system of systems (SoS) approach. SoS states that a plan is created through multiple smaller systems, creating complexity in the design and vice versa. A complex system can be assumed as the world in general, where two individual independent systems AI and carbon emissions, when in interaction, create a complex complementary and contradictory relation, adding to the convolution of the system. This connection is demonstrated by conducting a network analysis and calculating the carbon emissions of six machine learning (ML) algorithms and deep learning (DL) models with different datasets but the same hyperparameters on a carbon emission calculator created through AI algorithms. The primary idea of this study is to encourage the AI society to create efficient AI models that may be used without compromising environmental issues. The focus should be on practicing sustainable AI, that is, sustainability from data collection to model deployment, throughout the lifecycle of AI.