arXiv AI
Jul 8
ExplAIner: A Declarative Query Language for Explaining Classification Models
arXiv:2607. 06407v1 Announce Type: new Abstract: The XAI community has studied a wide range of queries and scores for explaining predictions of ML models.
Solving Hard XAI Queries Based on a Compiled Dual-Rail Encoding
The paper addresses the difficulty of generating abductive explanations for boolean classifiers, a key challenge in explainable AI. It demonstrates that even with efficient representations like Ordered Binary Decision Diagrams, many classes of abductive explanations remain computationally hard. The authors propose using a dual‑rail encoding of the classifier to enable efficient computation of these hard explanation classes.
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arXiv Machine Learning
Jun 16
We Need Explanation Cards to Connect Explanation Algorithms to the Real World
arXiv:2606. 16786v1 Announce Type: new Abstract: Algorithmic explanations are intended to help stakeholders understand opaque algorithmic decisions, but in practice, they often fall short.
arXiv AI
Aug 12
Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information
arXiv:2608. 10766v1 Announce Type: new Abstract: Explainable Artificial Intelligence (XAI) seeks to explain how an Artificial Intelligence (AI) system arrived at a particular decision.
arXiv AI
6d ago
A Synthetic Ground-Truth Framework for the Evaluation of Explainable AI Methods
The paper introduces a synthetic ground‑truth framework for evaluating explainable AI (XAI) methods, addressing the lack of reliable evaluation procedures. By using controlled interventions to create datasets where the importance of input components is known, the framework generates ground‑truth explanations that align with the model’s actual decision process. The authors apply this approach to binary images, tabular data, and time series, and find that nine popular XAI methods exhibit significant limitations, underscoring the need for intervention‑based benchmarks.
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
Aug 25
ExplainGuard: A Zero Trust Framework for Post-Hoc Explanation Integrity Guarantees in Blackbox XAI Models
arXiv:2608.21803v1 Announce Type: cross Abstract: As machine learning (ML) models are increasingly deployed in high-stakes environments, explainable AI (XAI) methods like SHAP and LIME have become es...
arXiv Machine Learning
Jun 10
Beyond Explaining Predictions: Logic-Based Explanations for Confidence in Machine Learning Models
arXiv:2606. 10347v1 Announce Type: new Abstract: Machine learning is increasingly used in critical domains, where both predictions and their associated confidence levels influence important decisions.
arXiv AI
Aug 25
Addressing the Selection Problem in Explainable AI
arXiv:2608.22356v1 Announce Type: new Abstract: Explainable AI (XAI) research has produced a plethora of explanation techniques, yet user studies repeatedly show that available explanations are not e...
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.
Hugging Face Trending Papers
Aug 23
Addressing the Selection Problem in Explainable AI
Explainable AI (XAI) research has produced a plethora of explanation techniques, yet user studies repeatedly show that available explanations are not effective in practice. We argue that, given the si...
arXiv AI
Jul 13
Explaining is Harder Than Predicting Alone: Evaluating Concept-based Explanations of MLLMs as ICL Visual Classifiers
arXiv:2605. 28215v2 Announce Type: replace Abstract: In-context learning (ICL) enables multimodal large language models (MLLMs) to classify images from a few labelled examples.
arXiv AI
Jun 11
An XAI View on Explainable ASP: Methods, Systems, and Perspectives
arXiv:2601. 14764v2 Announce Type: replace Abstract: Answer Set Programming (ASP) is a popular declarative reasoning and problem solving approach in symbolic AI.