A Unified Framework for Evaluating the Effectiveness and Enhancing the Transparency of Explainable AI Methods in Real-World Applications

• URL: http://arxiv.org/abs/2412.03884v1
• Date: Thu, 05 Dec 2024 05:30:10 GMT
• Title: A Unified Framework for Evaluating the Effectiveness and Enhancing the Transparency of Explainable AI Methods in Real-World Applications
• Authors: Md. Ariful Islam, M. F. Mridha, Md Abrar Jahin, Nilanjan Dey,
• Abstract summary: "Black box" characteristic of AI models constrains interpretability, transparency, and reliability. This study presents a unified XAI evaluation framework to evaluate correctness, interpretability, robustness, fairness, and completeness of explanations generated by AI models.
• Score: 2.0681376988193843
• License:
• Abstract: The rapid advancement of deep learning has resulted in substantial advancements in AI-driven applications; however, the "black box" characteristic of these models frequently constrains their interpretability, transparency, and reliability. Explainable artificial intelligence (XAI) seeks to elucidate AI decision-making processes, guaranteeing that explanations faithfully represent the model's rationale and correspond with human comprehension. Despite comprehensive research in XAI, a significant gap persists in standardized procedures for assessing the efficacy and transparency of XAI techniques across many real-world applications. This study presents a unified XAI evaluation framework incorporating extensive quantitative and qualitative criteria to systematically evaluate the correctness, interpretability, robustness, fairness, and completeness of explanations generated by AI models. The framework prioritizes user-centric and domain-specific adaptations, hence improving the usability and reliability of AI models in essential domains. To address deficiencies in existing evaluation processes, we suggest defined benchmarks and a systematic evaluation pipeline that includes data loading, explanation development, and thorough method assessment. The suggested framework's relevance and variety are evidenced by case studies in healthcare, finance, agriculture, and autonomous systems. These provide a solid basis for the equitable and dependable assessment of XAI methodologies. This paradigm enhances XAI research by offering a systematic, flexible, and pragmatic method to guarantee transparency and accountability in AI systems across many real-world contexts.

Related papers

• A Survey on Human-Centered Evaluation of Explainable AI Methods in Clinical Decision Support Systems [45.89954090414204]
  This paper provides a survey of human-centered evaluations of Explainable AI methods in Clinical Decision Support Systems. Our findings reveal key challenges in the integration of XAI into healthcare and propose a structured framework to align the evaluation methods of XAI with the clinical needs of stakeholders.
  arXiv  Detail & Related papers  (2025-02-14T01:21:29Z)
• From Aleatoric to Epistemic: Exploring Uncertainty Quantification Techniques in Artificial Intelligence [19.369216778200034]
  Uncertainty quantification (UQ) is a critical aspect of artificial intelligence (AI) systems. This review explores the evolution of uncertainty quantification techniques in AI. We examine the diverse applications of UQ across various fields, emphasizing its impact on decision-making, predictive accuracy, and system robustness.
  arXiv  Detail & Related papers  (2025-01-05T23:14:47Z)
• Explainable Artificial Intelligence: A Survey of Needs, Techniques, Applications, and Future Direction [5.417632175667161]
  Explainable Artificial Intelligence (XAI) addresses challenges by providing explanations for how these models make decisions and predictions. Existing studies have examined the fundamental concepts of XAI, its general principles, and the scope of XAI techniques. This paper provides a comprehensive literature review encompassing common terminologies and definitions, the need for XAI, beneficiaries of XAI, a taxonomy of XAI methods, and the application of XAI methods in different application areas.
  arXiv  Detail & Related papers  (2024-08-30T21:42:17Z)
• SCENE: Evaluating Explainable AI Techniques Using Soft Counterfactuals [0.0]
  This paper introduces SCENE (Soft Counterfactual Evaluation for Natural language Explainability), a novel evaluation method. By focusing on token-based substitutions, SCENE creates contextually appropriate and semantically meaningful Soft Counterfactuals. SCENE provides valuable insights into the strengths and limitations of various XAI techniques.
  arXiv  Detail & Related papers  (2024-08-08T16:36:24Z)
• Robustness of Explainable Artificial Intelligence in Industrial Process Modelling [43.388607981317016]
  We evaluate current XAI methods by scoring them based on ground truth simulations and sensitivity analysis. We show the differences between XAI methods in their ability to correctly predict the true sensitivity of the modeled industrial process.
  arXiv  Detail & Related papers  (2024-07-12T09:46:26Z)
• Explainable AI for Enhancing Efficiency of DL-based Channel Estimation [1.0136215038345013]
  Support of artificial intelligence based decision-making is a key element in future 6G networks. In such applications, using AI as black-box models is risky and challenging. We propose a novel-based XAI-CHEST framework that is oriented toward channel estimation in wireless communications.
  arXiv  Detail & Related papers  (2024-07-09T16:24:21Z)
• Can you trust your explanations? A robustness test for feature attribution methods [42.36530107262305]
  The field of Explainable AI (XAI) has seen a rapid growth but the usage of its techniques has at times led to unexpected results. We will show how leveraging manifold hypothesis and ensemble approaches can be beneficial to an in-depth analysis of the robustness.
  arXiv  Detail & Related papers  (2024-06-20T14:17:57Z)
• Evaluating General-Purpose AI with Psychometrics [43.85432514910491]
  We discuss the need for a comprehensive and accurate evaluation of general-purpose AI systems such as large language models. Current evaluation methodology, mostly based on benchmarks of specific tasks, falls short of adequately assessing these versatile AI systems. To tackle these challenges, we suggest transitioning from task-oriented evaluation to construct-oriented evaluation.
  arXiv  Detail & Related papers  (2023-10-25T05:38:38Z)
• Counterfactual Explanations as Interventions in Latent Space [62.997667081978825]
  Counterfactual explanations aim to provide to end users a set of features that need to be changed in order to achieve a desired outcome. Current approaches rarely take into account the feasibility of actions needed to achieve the proposed explanations. We present Counterfactual Explanations as Interventions in Latent Space (CEILS), a methodology to generate counterfactual explanations.
  arXiv  Detail & Related papers  (2021-06-14T20:48:48Z)
• An interdisciplinary conceptual study of Artificial Intelligence (AI) for helping benefit-risk assessment practices: Towards a comprehensive qualification matrix of AI programs and devices (pre-print 2020) [55.41644538483948]
  This paper proposes a comprehensive analysis of existing concepts coming from different disciplines tackling the notion of intelligence. The aim is to identify shared notions or discrepancies to consider for qualifying AI systems.
  arXiv  Detail & Related papers  (2021-05-07T12:01:31Z)
• Why model why? Assessing the strengths and limitations of LIME [0.0]
  This paper examines the effectiveness of the Local Interpretable Model-Agnostic Explanations (LIME) xAI framework. LIME is one of the most popular model agnostic frameworks found in the literature. We show how LIME can be used to supplement conventional performance assessment methods.
  arXiv  Detail & Related papers  (2020-11-30T21:08:07Z)

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.