X-SHIELD: Regularization for eXplainable Artificial Intelligence

• URL: http://arxiv.org/abs/2404.02611v2
• Date: Thu, 14 Nov 2024 22:53:12 GMT
• Title: X-SHIELD: Regularization for eXplainable Artificial Intelligence
• Authors: Iván Sevillano-García, Julián Luengo, Francisco Herrera,
• Abstract summary: XAI may be used to improve model performance while boosting its explainability. Within this family, we propose the XAI - SHIELD(X-SHIELD), a regularization for explainable artificial intelligence. The improvement is validated through experiments comparing models with and without the X-SHIELD regularization.
• Score: 9.658282892513386
• License:
• Abstract: As artificial intelligence systems become integral across domains, the demand for explainability grows, the called eXplainable artificial intelligence (XAI). Existing efforts primarily focus on generating and evaluating explanations for black-box models while a critical gap in directly enhancing models remains through these evaluations. It is important to consider the potential of this explanation process to improve model quality with a feedback on training as well. XAI may be used to improve model performance while boosting its explainability. Under this view, this paper introduces Transformation - Selective Hidden Input Evaluation for Learning Dynamics (T-SHIELD), a regularization family designed to improve model quality by hiding features of input, forcing the model to generalize without those features. Within this family, we propose the XAI - SHIELD(X-SHIELD), a regularization for explainable artificial intelligence, which uses explanations to select specific features to hide. In contrast to conventional approaches, X-SHIELD regularization seamlessly integrates into the objective function enhancing model explainability while also improving performance. Experimental validation on benchmark datasets underscores X-SHIELD's effectiveness in improving performance and overall explainability. The improvement is validated through experiments comparing models with and without the X-SHIELD regularization, with further analysis exploring the rationale behind its design choices. This establishes X-SHIELD regularization as a promising pathway for developing reliable artificial intelligence regularization.

Related papers

• Learning to Generate and Evaluate Fact-checking Explanations with Transformers [10.970249299147866]
  Research contributes to the field of Explainable Artificial Antelligence (XAI) We develop transformer-based fact-checking models that contextualise and justify their decisions by generating human-accessible explanations. We emphasise the need for aligning Artificial Intelligence (AI)-generated explanations with human judgements.
  arXiv  Detail & Related papers  (2024-10-21T06:22:51Z)
• On the Modeling Capabilities of Large Language Models for Sequential Decision Making [52.128546842746246]
  Large pretrained models are showing increasingly better performance in reasoning and planning tasks. We evaluate their ability to produce decision-making policies, either directly, by generating actions, or indirectly. In environments with unfamiliar dynamics, we explore how fine-tuning LLMs with synthetic data can significantly improve their reward modeling capabilities.
  arXiv  Detail & Related papers  (2024-10-08T03:12:57Z)
• Enhancing Feature Selection and Interpretability in AI Regression Tasks Through Feature Attribution [38.53065398127086]
  This study investigates the potential of feature attribution methods to filter out uninformative features in input data for regression problems. We introduce a feature selection pipeline that combines Integrated Gradients with k-means clustering to select an optimal set of variables from the initial data space. To validate the effectiveness of this approach, we apply it to a real-world industrial problem - blade vibration analysis in the development process of turbo machinery.
  arXiv  Detail & Related papers  (2024-09-25T09:50:51Z)
• 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)
• EXACT: Towards a platform for empirically benchmarking Machine Learning model explanation methods [1.6383837447674294]
  This paper brings together various benchmark datasets and novel performance metrics in an initial benchmarking platform. Our datasets incorporate ground truth explanations for class-conditional features. This platform assesses the performance of post-hoc XAI methods in the quality of the explanations they produce.
  arXiv  Detail & Related papers  (2024-05-20T14:16:06Z)
• QualEval: Qualitative Evaluation for Model Improvement [82.73561470966658]
  We propose QualEval, which augments quantitative scalar metrics with automated qualitative evaluation as a vehicle for model improvement. QualEval uses a powerful LLM reasoner and our novel flexible linear programming solver to generate human-readable insights. We demonstrate that leveraging its insights, for example, improves the absolute performance of the Llama 2 model by up to 15% points relative.
  arXiv  Detail & Related papers  (2023-11-06T00:21:44Z)
• survex: an R package for explaining machine learning survival models [8.028581359682239]
  We introduce the survex R package, which provides a framework for explaining any survival model by applying artificial intelligence techniques. The capabilities of the proposed software encompass understanding and diagnosing survival models, which can lead to their improvement.
  arXiv  Detail & Related papers  (2023-08-30T16:14:20Z)
• Understanding Self-attention Mechanism via Dynamical System Perspective [58.024376086269015]
  Self-attention mechanism (SAM) is widely used in various fields of artificial intelligence. We show that intrinsic stiffness phenomenon (SP) in the high-precision solution of ordinary differential equations (ODEs) also widely exists in high-performance neural networks (NN) We show that the SAM is also a stiffness-aware step size adaptor that can enhance the model's representational ability to measure intrinsic SP.
  arXiv  Detail & Related papers  (2023-08-19T08:17:41Z)
• HyperImpute: Generalized Iterative Imputation with Automatic Model Selection [77.86861638371926]
  We propose a generalized iterative imputation framework for adaptively and automatically configuring column-wise models. We provide a concrete implementation with out-of-the-box learners, simulators, and interfaces.
  arXiv  Detail & Related papers  (2022-06-15T19:10:35Z)
• Beyond Explaining: Opportunities and Challenges of XAI-Based Model Improvement [75.00655434905417]
  Explainable Artificial Intelligence (XAI) is an emerging research field bringing transparency to highly complex machine learning (ML) models. This paper offers a comprehensive overview over techniques that apply XAI practically for improving various properties of ML models. We show empirically through experiments on toy and realistic settings how explanations can help improve properties such as model generalization ability or reasoning.
  arXiv  Detail & Related papers  (2022-03-15T15:44:28Z)
• Generative Counterfactuals for Neural Networks via Attribute-Informed Perturbation [51.29486247405601]
  We design a framework to generate counterfactuals for raw data instances with the proposed Attribute-Informed Perturbation (AIP) By utilizing generative models conditioned with different attributes, counterfactuals with desired labels can be obtained effectively and efficiently. Experimental results on real-world texts and images demonstrate the effectiveness, sample quality as well as efficiency of our designed framework.
  arXiv  Detail & Related papers  (2021-01-18T08:37:13Z)

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.