arXiv AI
Jul 17
Local Additive Feature Attribution: A Mathematical Taxonomy and Reporting Checklist
arXiv:2607. 14271v1 Announce Type: cross Abstract: Feature-attribution methods are central to explainable artificial intelligence.
Measuring Explainer Stability via Attribution Separability
arXiv:2608. 02697v1 Announce Type: cross Abstract: Attribution methods (AMs) assign an importance score to each feature and are widely adopted to explain black-box models.
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arXiv Machine Learning
Jun 3
Attribution via Distributional Paths for Information Revelation
arXiv:2606. 03885v1 Announce Type: new Abstract: Feature attribution methods explain predictions by assigning importance scores to input features.
arXiv AI
Aug 3
A Human-Centered Validation of the Explainability-Performance Coefficient
arXiv:2607. 29614v1 Announce Type: cross Abstract: The rapid adoption of deep learning models in high-risk domains has intensified the need for trustworthy Explainable Artificial Intelligence (XAI).
arXiv AI
Jun 2
A Monosemantic Attribution Framework for Stable Interpretability in Clinical Neuroscience Transformer-Based Language Models
arXiv:2601. 17952v2 Announce Type: replace-cross Abstract: Interpretability remains a key challenge for deploying language models (LM) in clinical settings such as progression diagnosis of Alzheimer disease, where early and trustworthy predictions are essential.
arXiv AI
Jul 21
Position: Explanation Stability Is a Property of the Model Method Pair, Not the Model
arXiv:2607. 16652v1 Announce Type: cross Abstract: This position paper argues that claims about explanation stability are scientifically invalid without cross method validation.
arXiv AI
Jun 12
On Pitfalls of $\textit{RemOve-And-Retrain}$: Data Processing Inequality Perspective
arXiv:2304. 13836v5 Announce Type: replace-cross Abstract: The RemOve-And-Retrain (ROAR) benchmark is widely used to evaluate feature attribution methods, yet its validity remains underexplored from an information-theoretic perspective.
arXiv AI
Jul 28
How Context Attribution Handles What the Model Already Knows
arXiv:2607. 23804v1 Announce Type: cross Abstract: Context attribution methods for large language models (LLMs) identify which input context contributes to the model response.
arXiv AI
Jul 17
Towards a Unified Multidimensional Explainability Metric: Evaluating Trustworthiness in AI Models
arXiv:2607. 14315v1 Announce Type: cross Abstract: In this paper, we present a comprehensive framework for assessing the explainability of various XAI methods, such as LIME and SHAP, across multiple datasets and machine learning models, with the ultimate goal of creating a unified multidimensional explainability score.
arXiv AI
Jul 21
The Failures of Marginal Influence-Based Attribution Methods for Global Time Series Explanations
arXiv:2607. 16236v1 Announce Type: cross Abstract: Explainability methods for time series models predominantly produce flat attribution scores: they quantify the direct influence of a feature at a timestamp by a scalar.
arXiv AI
Aug 10
MAC: A Conversion Rate Prediction Benchmark Featuring Labels Under Multiple Attribution Mechanisms
arXiv:2603. 02184v2 Announce Type: replace-cross Abstract: Multi-attribution learning (MAL), which enhances model performance by learning from conversion labels yielded by multiple attribution mechanisms, has emerged as a promising learning paradigm for conversion rate (CVR) prediction.
arXiv Machine Learning
Jun 16
Enhancing Visual Feature Attribution via Weighted Integrated Gradients
arXiv:2505. 03201v4 Announce Type: replace-cross Abstract: Integrated Gradients (IG) is a widely used attribution method in explainable AI, particularly in computer vision applications where reliable feature attribution is essential.
arXiv AI
Jun 30
Are LLMs Reliable Rankers? Rank Manipulation via Two-Stage Token Optimization
arXiv:2510. 06732v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly used as rerankers in information retrieval, yet their ranking behavior can be steered by small, natural-sounding prompts.