EXPLAINABLE AI FOR TRANSPARENT EMISSION REDUCTION DECISION-MAKING
Jeng-Jui Du, Shiu-Chu Chiu · Frontiers in Environmental Research · 2024
This paper examines the critical role of Explainable AI (XAI) in enhancing transparency in emission reduction decision-making processes. As climate change poses an urgent global challenge, effective strategies for reducing greenhouse gas emissions are essential for mitigating its impacts. Artificial Intelligence has emerged as a powerful tool in environmental management, facilitating data analysis and optimizing emission reduction efforts. However, the increasing reliance on AI raises concerns about transparency and accountability, which are vital for gaining public trust. This paper defines XAI and explores its methodologies, emphasizing their potential to improve stakeholder engagement and decision-making in environmental policy. By synthesizing existing literature and case studies, we highlight the importance of explainability in fostering trust among stakeholders and ensuring effective and accountable emission reduction strategies. The findings contribute to the ongoing discourse on the ethical and practical implications of AI in environmental governance and underscore the necessity of incorporating XAI into future emission reduction initiatives.