Explainable Artificial Intelligence: Analysis of Methodologies and Applications

Bibliography Reference

Format:

y Claudia Pons, M. C. P. (2025). Explainable Artificial Intelligence: Analysis of Methodologies and Applications. Journal of Computer Science & Technology, 25(2), 75–86. https://doi.org/https://doi.org/10.24215/16666038.25.e07

Publication Abstract

Explainability is essential in healthcare, finance, and security, where black-box models can undermine trust and decisions. Recent advances in eXplainable Artificial Intelligence (XAI) across structured/tabular data, computer vision, and natural language processing are surveyed. Thirty articles (2022–2024) were selected through a structured search with explicit inclusion criteria, and emerging approaches are compared with established techniques such as LIME and SHAP, alongside rule-, logic-, and ontology-based methods. Methods are organized along key dimensions—post-hoc vs. ante-hoc, model-agnostic vs. model-specific, scope, problem type, input data, and output format—and their effectiveness and applicability are evaluated. The review highlights innovations including spatially explainable architectures (e.g., SAMCNet) and entropy-based logic explanations, and identifies persistent challenges in robustness, cross-domain generalization, and deployment. Overall, findings consolidate the evolving XAI landscape and indicate directions toward reproducible techniques that strengthen transparency, accountability, and user trust in AI systems.

BibTeX Source Entry

@article{50246362,
  doi = {https://doi.org/10.24215/16666038.25.e07},
  note = {},
  year = {2025},
  month = {},
  pages = {75-86},
  title = {Explainable  Artificial  Intelligence:  Analysis  of  Methodologies  and Applications},
  author = {María Cecilia Pezzini y Claudia Pons},
  number = {2},
  volume = {25},
  journal = {Journal of Computer Science & Technology},
  ranking = {Q3},
  abstract = {Explainability is essential in healthcare, finance, and security, where black-box models can undermine trust and decisions. Recent advances in eXplainable Artificial Intelligence (XAI) across structured/tabular data, computer vision, and natural language processing are surveyed. Thirty articles (2022–2024) were selected through a structured search with explicit inclusion criteria, and emerging approaches are compared with established techniques such as LIME and SHAP, alongside rule-, logic-, and ontology-based methods. Methods are organized along key dimensions—post-hoc vs. ante-hoc, model-agnostic vs. model-specific, scope, problem type, input data, and output format—and their effectiveness and applicability are evaluated. The review highlights innovations including spatially explainable architectures (e.g., SAMCNet) and entropy-based logic explanations, and identifies persistent challenges in robustness, cross-domain generalization, and deployment. Overall, findings consolidate the evolving XAI landscape and indicate directions toward reproducible techniques that strengthen transparency, accountability, and user trust in AI systems.},
}