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.
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.
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arXiv Machine Learning
Jun 16
Evaluating Local Explainability Metrics for Machine Learning Models on Tabular Data
arXiv:2605. 27618v2 Announce Type: replace Abstract: Despite the wide use of explainability techniques to attempt to understand the behavior of Artificial Intelligence (AI), the generated explanations may not always be reliable.
arXiv AI
Aug 5
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.
Hugging Face Trending Papers
Jun 29
Decision-Value Attribution in Predict-then-Optimize Systems
Predictive models are increasingly embedded in operational decision-making, yet standard explanation methods typically explain forecasts rather than the decisions those forecasts induce. This distinction is important in predict-then-optimize systems: large forecast changes may leave the optimizer's action unchanged, while small changes can alter the selected decision and its realized value.
arXiv AI
Jul 29
TaylorPODA: A Taylor Expansion-Based Method to Improve Post-Hoc Attributions for Opaque Models
arXiv:2507. 10643v4 Announce Type: replace-cross Abstract: Post-hoc model-agnostic local attribution (LA) methods have been widely adopted to explain opaque AI models by quantifying feature-wise contributions.
arXiv Machine Learning
Jun 30
Decision-Value Attribution in Predict-then-Optimize Systems
arXiv:2606. 29878v1 Announce Type: new Abstract: Predictive models are increasingly embedded in operational decision-making, yet standard explanation methods typically explain forecasts rather than the decisions those forecasts induce.
arXiv AI
Jun 2
From Features to Actions: Explainability in Traditional and Agentic AI Systems
arXiv:2602. 06841v4 Announce Type: replace Abstract: Over the last decade, Explainable AI has primarily focused on interpreting individual model predictions, producing post-hoc explanations that relate inputs to outputs under a fixed decision structure.
arXiv AI
Jun 29
OperatorSHAP: Fast and Accurate Shapley Value Estimation for Neural Operators
arXiv:2606. 28065v1 Announce Type: cross Abstract: Understanding model predictions is essential for physical applications, where outputs often inform safety-critical decisions, such as structural load assessment, weather warnings, and clinical diagnosis.
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 Machine Learning
Jul 21
MinShap: A Shapley-Based Framework for Feature Redundancy
arXiv:2604. 15107v2 Announce Type: replace-cross Abstract: Shapley values provide a flexible framework for attributing feature contributions to model predictions, but they are not naturally suited for feature selection: a feature may receive a positive attribution even when it is redundant given the remaining variables.
arXiv AI
Jun 10
Generative Explainability for Next-Generation Networks: LLM-Augmented XAI with Mutual Feature Interactions
arXiv:2606. 10942v1 Announce Type: cross Abstract: As artificial intelligence and machine learning (AI/ML) models become integral to network operations, their lack of transparency poses a significant barrier to operator trust.
Hugging Face Trending Papers
Jul 28
More Data, Worse Decisions? Preference Reversals in Neural Networks under Gram Incompatibility
Neural networks increasingly combine data across populations, time periods, and operating conditions to improve generalization. This raises a reliability question: whether a model refitted on pooled data preserves an action ordering supported by both sources.