XALIGN is a visual analytics workflow that simultaneously displays rule‑based explanations and multiple feature attribution methods (TreeSHAP, KernelSHAP, and LIME) for a single Random Forest model. By aligning these methods side by side, XALIGN makes agreements and contradictions between them visible, allowing users to compare explanations against domain knowledge. The approach was tested on the German Credit dataset and a meteorological classification task, where it uncovered discrepancies that single‑method analyses missed, and experts reported it helped identify influential features and reduce over‑trust in predictions.
XAI-Arena proposes using large language models (LLMs) as judges to evaluate the quality of explainable AI (XAI) explanations, aiming for reproducibility, scalability, and multidimensional assessment. The framework assesses dimensions such as simplicity, clarity, task adequacy, trust calibration, actionability, transparency, faithfulness, and overall interpretability across different datasets, models, and stakeholder personas. Human validation shows a strong positive correlation between LLM-generated and human ratings (Spearman's rho = .693, p < .001), supporting the viability of LLM-based evaluations.
By Yanfei Hu Fleischhauer, Alona Zharova, Nadja Klein, Stefan Feuerriegel
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.
By Kaivalya Rawal, Daria Onitiu, Brent Mittelstadt, Sandra Wachter, Chris Russell
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.
By Georgios Makridis, Georgios Fatouros, Athanasios Kiourtis, Dimitrios Kotios, Vasileios Koukos, Dimosthenis Kyriazis, Jonh Soldatos
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.
By Miquel Mir\'o-Nicolau, Francesco Spinnato, Riccardo Guidotti
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.
By Tom\'as Pereira, Jo\~ao Vitorino, Eva Maia, Isabel Pra\c{c}a
The paper argues that explainable AI (XAI) tools, intended to increase trust in machine learning models, often fail to do so and instead foster overreliance on incorrect predictions. It attributes this failure to the unfaithfulness of XAI methods, which oversimplify model internals and misalign explanations with the actual model behavior. The authors identify five major shortcomings in current faithfulness metrics and propose four research directions to improve their reliability.
By Marco Zullich, Emily Schiller, Ivan Gentile, David Dembinsky, Adriano Lucieri, Kilian G\"oller, Steffen Seitz
Det‑LIME is a detector‑aware, multi‑instance adaptation of LIME designed to explain black‑box object detectors used in marine mammal research. It generates instance‑specific, box‑aligned explanations by weighting detections, applying a proximity kernel, and using IoU‑based matching to track instances across perturbations. Evaluated on aerial drone imagery of harbor seals and a seabird case study, Det‑LIME outperformed vanilla LIME, Stabilized LIME, Deterministic LIME, and gradient‑based methods in Attribution Ratio and Max Saliency Hit Rate, offering higher‑resolution, instance‑aware explanations that aid debugging, data augmentation, and modeling improvements.
By Jiayi Zhou, David W. Johnston, Brinnae Bent
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.
By Kiarash Rezaei, Omran Ayoub, Sebastian Troia, Francesco Lelli, Paolo Monti, Carlos Natalino
arXiv:2608. 16259v1 Announce Type: cross Abstract: The rapid progress of image generation models calls for AI-generated image (AIGI) detectors that are not only accurate but also explainable and reliable.
By Bowen Deng, Jiahui Zhan, Yikun Ji, Haozhen Yan, Jianfu Zhang
The paper introduces a formal auditing framework to evaluate the robustness and fidelity of post‑hoc explainers such as SHAP and LIME. It defines a Trust Score that combines how stable an explanation is under small input perturbations with how well the highlighted features actually influence the model’s prediction. Experiments on a Madagascar malnutrition dataset show that even highly accurate models can produce unreliable explanations, and that fidelity scores degrade when models overfit.
By Rosa Elysabeth Ralinirina, Jean Christian Ralaivao, Niaiko Micha\"el Ralaivao, Alain Josu\'e Ratovondrahona, Thomas Mahatody
NOTAI.AI is an explainable AI-generated text detection system that goes beyond a simple binary label by showing which signals influenced its prediction. It combines sentence-level conditional probability curvature, a neural detector score, and interpretable stylometric and readability features in an XGBoost meta-classifier, and explains predictions using TreeSHAP feature contributions that can be turned into concise natural-language explanations. Evaluated on a balanced subset of RAID, the full model achieves 0.9685 F1 and receives 94.5–98.6% approval from model judges for the faithfulness of its explanations.
By Oleksandr Marchenko Breneur, Adelaide Danilov, Aria Nourbakhsh, Salima Lamsiyah