Ethical Considerations and Challenges in AI Adoption for Sustainable Energy

Artificial Intelligence (AI) holds immense potential to revolutionize various sectors, including sustainable energy, finance, grid management, transportation, and healthcare. By optimizing resource use, predicting maintenance needs, and enabling smarter energy distribution, AI can significantly contribute to achieving sustainable energy goals. However, integrating AI in sustainable energy systems also raises numerous ethical considerations and challenges that must be addressed to ensure equitable and responsible use. The rapidly growing carbon footprint of AI, driven by the substantial energy required to train increasingly complex Machine Learning (ML) models, has raised significant concerns. These concerns raise questions about the true potential of AI in achieving sustainability goals. This chapter explores the ethical implications of AI adoption in the sustainable energy sector. It critically examines the ethical risks and responsibilities surrounding the integration of AI into sustainable energy infrastructure, addressing issues such as data privacy, algorithmic bias, transparency, and socioeconomic impact. As AI algorithms become integral to energy systems, stakeholders must have access to information about model architectures, data sources, and decision criteria. Bias and fairness represent another crucial dimension. AI models can inadvertently perpetuate biases present in training data, potentially exacerbating existing inequalities. Researchers emphasize the need for robust approaches to mitigate bias and ensure equitable access to energy resources. Privacy and data security are ongoing challenges in the AI-driven energy landscape. These systems rely on vast amounts of data, and protecting user privacy while securing sensitive information is paramount. Differential privacy and federated learning have been proposed to address these concerns. Striking a balance between energy efficiency and computational demands is also critical. While AI can optimize energy consumption, it requires substantial computational resources. Researchers have explored the environmental impact of AI deployment, emphasizing the need for sustainable practices. In conclusion, the responsible adoption of AI in sustainable energy requires interdisciplinary collaboration, robust regulations, and continuous ethical scrutiny. Policymakers, researchers, and practitioners must draw insights from relevant studies and frameworks to effectively navigate these challenges. The adoption of Green AI is a better alternative to conventional AI for producing accurate results with lower computational costs.