Review
Version 1
This version is not peer-reviewed
A Literature Review of Explainable Tabular Data
Version 1
: Received: 7 August 2024 / Approved: 7 August 2024 / Online: 8 August 2024 (12:34:24 CEST)
How to cite: O'Brien Quinn, H.; Sedky, M.; Francis, J.; Streeton, M. A Literature Review of Explainable Tabular Data. Preprints 2024, 2024080556. https://doi.org/10.20944/preprints202408.0556.v1 O'Brien Quinn, H.; Sedky, M.; Francis, J.; Streeton, M. A Literature Review of Explainable Tabular Data. Preprints 2024, 2024080556. https://doi.org/10.20944/preprints202408.0556.v1
Abstract
Explainable Artificial Intelligence (XAI) plays a vital role in increasing transparency and trust in machine learning models, particularly when applied to tabular data which is used in domains such as finance, healthcare, and marketing. This paper presents an extensive survey of XAI techniques used with tabular data and aims to analyze recent research developments since 2021. The survey classes and describes several XAI techniques pertinent to tabular data, it identifies challenges specific to this domain, and explores potential applications and emerging trends. Future research directions are outlined, concentrating on the need for clear definitions of terminology used, data security, user-centric explanations, enhanced interaction, robust evaluation metrics, and advancements in adversarial example-based analysis. The aim is to contribute to the evolving field of XAI, and provide insights for effective, trustworthy, and transparent decision-making using tabular data.
Keywords
Explainable Artificial Intelligence (XAI); tabular data, interpretable; machine learning (ML)
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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