Using NLP transformer models to evaluate the relationship between ethical principles in finance and machine learning

Abstract

While the ethical principles of finance are well known in the literature, they are not sufficiently evaluated in the context of machine learning (ML). We use natural language processing (NLP) transformer models to quantitatively evaluate the relationships between the ethical principles of finance and the ethical principles of ML. To the best of our knowledge, such analysis has not been performed in the literature. We assess the performance of more than 80 state-of-the-art (SOTA) transformer models in capturing semantic similarity between the definitions of finance and ML ethics principles. The computational results demonstrate the ability of various transformers to address semantic similarity when comparing the definitions of finance and ML ethics. The results reveal that the NLI-DistilRoBERTa-Base-v2 model has the best performance in this task. The analysis can be beneficial to identify the principles of finance ethics that exhibit the strongest influence on ML ethics and vice-versa.