Using NLP transformer models to evaluate the relationship between ethical principles in finance and machine learning
Date Issued
2023-03-12
Author(s)
Rizinski, Maryan
Chitkushev, Lubomir
Vodenska, Irena
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.
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.
Subjects
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