Integrating eXplainable AI in Healthcare: A Web Application Framework for Advancing the One Health Paradigm

WALS24 - The 3rd International Workshop on Web Applications for Life Sciences - In conjunction with the 24th International Conference on Web Engineering (ICWE 2024) - -2024

Authors

Danese Danilo, Di Noia Tommaso, Lombardi Angela, Lofù Domenico, Nazary Fatemeh, Sardone Rodolfo, Sorino Paolo

Abstract

Abstract Machine learning (ML) rapidly gains increasing interest due to the continuous improvements in performance. ML is used in many different applications to support human users. The representational power of ML models allows solving difficult tasks, while making them impossible to be understood by humans. This provides room for possible errors and limits the full potential of ML, as it cannot be applied in critical environments. In this paper, we propose employing Explainable AI (xAI) for both model and data set refinement, in order to introduce trust and comprehensibility. Model refinement utilizes xAI for providing insights to inner workings of an ML model, for identifying limitations and for deriving potential improvements. Similarly, xAI is used in data set refinement to detect and resolve problems of the training data.

Download: WALS24__LA_Camera_Ready.pdf

DOI

https://doi.org/https://doi.org/10.1007/978-3-031-75110-3_7

BibTex references

@Article{DDLLNSS24,
  author       = "Danese, Danilo and Di Noia, Tommaso and Lombardi, Angela and Lof\`u, Domenico and Nazary, Fatemeh and Sardone, Rodolfo and Sorino, Paolo",
  title        = "Integrating eXplainable AI in Healthcare: A Web Application Framework for Advancing the One Health Paradigm",
  journal      = "WALS24 - The 3rd International Workshop on Web Applications for Life Sciences - In conjunction with the 24th International Conference on Web Engineering (ICWE 2024)",
  year         = "2024",
  url          = "http://sisinflab.poliba.it/Publications/2024/DDLLNSS24"

}