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@ARTICLE{,
    author = {Contreras, Victor H. and Schumacher, Michael and Calvaresi, Davide},
  keywords = {Local explainability {\textperiodcentered} Global explainability {\textperiodcentered} Feature rank- ing {\textperiodcentered} rule extraction},
     month = jun,
     title = {Integration of local and global features explanation with global rules extraction and generation tools},
   journal = {Post-proceedings of EXTRAAMAS 2022},
      year = {2022},
  abstract = {Widely used in a growing number of domains, Deep Learning predictors are achieving remarkable results. However, the lack of trans- parency (i.e., opacity) of their inner mechanisms has raised trust and employability concerns. Nevertheless, several approaches fostering mod- els of interpretability and explainability have been developed in the last decade. This paper combines approaches for local feature explanation (i.e., Contextual Importance and Utility – CIU) and global feature ex- planation (i.e., Explainable Layers) with a rule extraction system, namely ECLAIRE. The proposed pipeline has been tested in four scenarios em- ploying a breast cancer diagnosis dataset. The results show improvements such as the production of more human-interpretable rules and adherence of the produced rules with the original model.}
}