  Large Language Models (LLMs) have emerged as potent tools for advancing the
United Nations' Sustainable Development Goals (SDGs). However, the attitudinal
disparities between LLMs and humans towards these goals can pose significant
challenges. This study conducts a comprehensive review and analysis of the
existing literature on the attitudes of LLMs towards the 17 SDGs, emphasizing
the comparison between their attitudes and support for each goal and those of
humans. We examine the potential disparities, primarily focusing on aspects
such as understanding and emotions, cultural and regional differences, task
objective variations, and factors considered in the decision-making process.
These disparities arise from the underrepresentation and imbalance in LLM
training data, historical biases, quality issues, lack of contextual
understanding, and skewed ethical values reflected. The study also investigates
the risks and harms that may arise from neglecting the attitudes of LLMs
towards the SDGs, including the exacerbation of social inequalities, racial
discrimination, environmental destruction, and resource wastage. To address
these challenges, we propose strategies and recommendations to guide and
regulate the application of LLMs, ensuring their alignment with the principles
and goals of the SDGs, and therefore creating a more just, inclusive, and
sustainable future.
