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Interpretable NLP Models: Towards Transparent and Trustworthy AI Systems

EasyChair Preprint no. 12272

7 pagesDate: February 24, 2024


As natural language processing (NLP) models become increasingly integral to various applications, ensuring their interpretability is paramount for fostering trust and understanding. This paper delves into the critical importance of interpretability in NLP models, advocating for transparent and trustworthy AI systems. Ultimately, this paper underscores the imperative of interpretability in NLP as a cornerstone for building AI systems that are not only powerful but also ethically sound and trustworthy. As AI technologies permeate various sectors, stakeholders demand explanations for the decisions made by these models, especially in sensitive domains such as healthcare, finance, and legal systems.

Keyphrases: language, natural, processing

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Kurez Oroy and Evan Bruze},
  title = {Interpretable NLP Models: Towards Transparent and Trustworthy AI Systems},
  howpublished = {EasyChair Preprint no. 12272},

  year = {EasyChair, 2024}}
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