XALIGN is a visual analytics workflow that displays rule-based explanations alongside feature attribution methods (TreeSHAP, KernelSHAP, and LIME) for the same Random Forest model, making agreements and contradictions between methods visible. In case studies on German Credit and meteorological data, XALIGN uncovered discrepancies that single-method analyses would miss. An expert evaluation showed that specialists found XALIGN helpful for identifying influential features and preventing over-trust in predictions, though linking rules to attributions was more challenging for those without an XAI background.
By Bahavathy Kathirgamanathan, Gennady Andrienko, Natalia Andrienko, Tatiana von Landesberger
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
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: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 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
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
arXiv:2607. 14152v1 Announce Type: cross Abstract: The persuasive power of data visualizations can go awry: for instance, in an explainable AI (XAI) context, visualizations can produce over-trust of predictive models.
By Michael Correll, Lucy Havens, Mahsan Nourani
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
The paper introduces a hypothesis‑testing framework that embeds feature importance methods (FIMs) within a Weight of Evidence (WoE) analysis. By quantifying how strongly observed evidence supports a given hypothesis—whether from domain knowledge, ground truth, or the FIM itself—the approach evaluates FIM alignment and variability. The authors provide theoretical links between WoE and attribution variance and demonstrate the method on LIME and SHAP explanations across varied reference hypotheses.
By Eddie Conti, Claudio Daka, \'Alvaro Parafita, Antonio L. Alfeo, Axel Brando, Mario G. C. A. Cimino
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...
By Maraz Mia, Shovan Roy, Mir Mehedi A. Pritom, Maanak Gupta
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. Existing explainable artificial intelligence (XAI) techniques often fail to bridge this gap for non-specialists, producing technical outputs that are difficult to translate into actionable insights.