  Environmental, Social, and Governance (ESG) datasets are frequently plagued
by significant data gaps, leading to inconsistencies in ESG ratings due to
varying imputation methods. This paper explores the application of established
machine learning techniques for imputing missing data in a real-world ESG
dataset, emphasizing the quantification of uncertainty through prediction
intervals. By employing multiple imputation strategies, this study assesses the
robustness of imputation methods and quantifies the uncertainty associated with
missing data. The findings highlight the importance of probabilistic machine
learning models in providing better understanding of ESG scores, thereby
addressing the inherent risks of wrong ratings due to incomplete data. This
approach improves imputation practices to enhance the reliability of ESG
ratings.
