Explainable Artificial Intelligence and Multicollinearity : A Mini Review of Current Approaches

• URL: http://arxiv.org/abs/2406.11524v1
• Date: Mon, 17 Jun 2024 13:26:53 GMT
• Title: Explainable Artificial Intelligence and Multicollinearity : A Mini Review of Current Approaches
• Authors: Ahmed M Salih,
• Abstract summary: Explainable Artificial Intelligence (XAI) methods help to understand the internal mechanism of machine learning models. The list of informative features is one of the most common output of XAI methods. Multicollinearity is one of the big issue that should be considered when XAI generates the explanation.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Explainable Artificial Intelligence (XAI) methods help to understand the internal mechanism of machine learning models and how they reach a specific decision or made a specific action. The list of informative features is one of the most common output of XAI methods. Multicollinearity is one of the big issue that should be considered when XAI generates the explanation in terms of the most informative features in an AI system. No review has been dedicated to investigate the current approaches to handle such significant issue. In this paper, we provide a review of the current state-of-the-art approaches in relation to the XAI in the context of recent advances in dealing with the multicollinearity issue. To do so, we searched in three repositories that are: Web of Science, Scopus and IEEE Xplore to find pertinent published papers. After excluding irrelevant papers, seven papers were considered in the review. In addition, we discuss the current XAI methods and their limitations in dealing with the multicollinearity and suggest future directions.

Related papers

• More Questions than Answers? Lessons from Integrating Explainable AI into a Cyber-AI Tool [1.5711133309434766]
  We describe a preliminary case study on the use of XAI for source code classification. We find that the outputs of state-of-the-art saliency explanation techniques are lost in translation when interpreted by people with little AI expertise. We outline unaddressed gaps in practical and effective XAI, then touch on how emerging technologies like Large Language Models (LLMs) could mitigate these existing obstacles.
  arXiv  Detail & Related papers  (2024-08-08T20:09:31Z)
• Explainable Generative AI (GenXAI): A Survey, Conceptualization, and Research Agenda [1.8592384822257952]
  We elaborate on why XAI has gained importance with the rise of GenAI and its challenges for explainability research. We also unveil novel and emerging desiderata that explanations should fulfill, covering aspects such as verifiability, interactivity, security, and cost.
  arXiv  Detail & Related papers  (2024-04-15T08:18:16Z)
• Gradient based Feature Attribution in Explainable AI: A Technical Review [13.848675695545909]
  Surge in black-box AI models has prompted the need to explain the internal mechanism and justify their reliability. gradient based explanations can be directly adopted for neural network models. We introduce both human and quantitative evaluations to measure algorithm performance.
  arXiv  Detail & Related papers  (2024-03-15T15:49:31Z)
• Usable XAI: 10 Strategies Towards Exploiting Explainability in the LLM Era [77.174117675196]
  XAI is being extended towards Large Language Models (LLMs) This paper analyzes how XAI can benefit LLMs and AI systems. We introduce 10 strategies, introducing the key techniques for each and discussing their associated challenges.
  arXiv  Detail & Related papers  (2024-03-13T20:25:27Z)
• Opening the Black-Box: A Systematic Review on Explainable AI in Remote Sensing [51.524108608250074]
  Black-box machine learning approaches have become a dominant modeling paradigm for knowledge extraction in remote sensing. We perform a systematic review to identify the key trends in the field and shed light on novel explainable AI approaches. We also give a detailed outlook on the challenges and promising research directions.
  arXiv  Detail & Related papers  (2024-02-21T13:19:58Z)
• Is Task-Agnostic Explainable AI a Myth? [0.0]
  Our work serves as a framework for unifying the challenges of contemporary explainable AI (XAI) We demonstrate that while XAI methods provide supplementary and potentially useful output for machine learning models, researchers and decision-makers should be mindful of their conceptual and technical limitations. We examine three XAI research avenues spanning image, textual, and graph data, covering saliency, attention, and graph-type explainers.
  arXiv  Detail & Related papers  (2023-07-13T07:48:04Z)
• Towards Human Cognition Level-based Experiment Design for Counterfactual Explanations (XAI) [68.8204255655161]
  The emphasis of XAI research appears to have turned to a more pragmatic explanation approach for better understanding. An extensive area where cognitive science research may substantially influence XAI advancements is evaluating user knowledge and feedback. We propose a framework to experiment with generating and evaluating the explanations on the grounds of different cognitive levels of understanding.
  arXiv  Detail & Related papers  (2022-10-31T19:20:22Z)
• Connecting Algorithmic Research and Usage Contexts: A Perspective of Contextualized Evaluation for Explainable AI [65.44737844681256]
  A lack of consensus on how to evaluate explainable AI (XAI) hinders the advancement of the field. We argue that one way to close the gap is to develop evaluation methods that account for different user requirements.
  arXiv  Detail & Related papers  (2022-06-22T05:17:33Z)
• OmniXAI: A Library for Explainable AI [98.07381528393245]
  We introduce OmniXAI, an open-source Python library of eXplainable AI (XAI) It offers omni-way explainable AI capabilities and various interpretable machine learning techniques. For practitioners, the library provides an easy-to-use unified interface to generate the explanations for their applications.
  arXiv  Detail & Related papers  (2022-06-01T11:35:37Z)
• Explainable AI: current status and future directions [11.92436948211501]
  Explainable Artificial Intelligence (XAI) is an emerging area of research in the field of Artificial Intelligence (AI) XAI can explain how AI obtained a particular solution and can also answer other "wh" questions. This paper provides an overview of these techniques from a multimedia (i.e., text, image, audio, and video) point of view.
  arXiv  Detail & Related papers  (2021-07-12T08:42:19Z)
• Explainability in Deep Reinforcement Learning [68.8204255655161]
  We review recent works in the direction to attain Explainable Reinforcement Learning (XRL) In critical situations where it is essential to justify and explain the agent's behaviour, better explainability and interpretability of RL models could help gain scientific insight on the inner workings of what is still considered a black box.
  arXiv  Detail & Related papers  (2020-08-15T10:11:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.