Cover Image

Evaluating Explainability in Safety-Critical ATR Systems: Limitations of Post-Hoc Methods and Paths Toward Robust XAI

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Buhrmester, Vanessa, Muench, David, Bulatov, Dimitri, Arens, Michael
Format:	Preprint
Published:	2026
Subjects:	Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

BarkXAI: A Lightweight Post-Hoc Explainable Method for Tree Species Classification with Quantifiable Concepts
by: Huang, Yunmei, et al.
Published: (2025)

XAI-Units: Benchmarking Explainability Methods with Unit Tests
by: Lee, Jun Rui, et al.
Published: (2025)

The State of Post-Hoc Local XAI Techniques for Image Processing: Challenges and Motivations
by: Poh, Rech Leong Tian, et al.
Published: (2025)

An XAI View on Explainable ASP: Methods, Systems, and Perspectives
by: Eiter, Thomas, et al.
Published: (2026)

EvalxNLP: A Framework for Benchmarking Post-Hoc Explainability Methods on NLP Models
by: Dhaini, Mahdi, et al.
Published: (2025)

Comparing Post-Hoc Explainable AI Methods for Interpreting Black-Box EEG Models in Depression Detection
by: Šarčević, Antonia, et al.
Published: (2026)

Assessing Model-Agnostic XAI Methods against EU AI Act Explainability Requirements
by: Sovrano, Francesco, et al.
Published: (2026)

Explanations as Bias Detectors: A Critical Study of Local Post-hoc XAI Methods for Fairness Exploration
by: Papanikou, Vasiliki, et al.
Published: (2025)

Exploring SAIG Methods for an Objective Evaluation of XAI
by: Miró-Nicolau, Miquel, et al.
Published: (2026)

xai_evals : A Framework for Evaluating Post-Hoc Local Explanation Methods
by: Seth, Pratinav, et al.
Published: (2025)

Finding the right XAI method -- A Guide for the Evaluation and Ranking of Explainable AI Methods in Climate Science
by: Bommer, Philine, et al.
Published: (2023)

Towards Explainable Artificial Intelligence (XAI): A Data Mining Perspective
by: Xiong, Haoyi, et al.
Published: (2024)

Smooth InfoMax -- Towards Easier Post-Hoc Interpretability
by: Denoodt, Fabian, et al.
Published: (2024)

Operationalizing Fairness: Post-Hoc Threshold Optimization Under Hard Resource Limits
by: Singh, Moirangthem Tiken, et al.
Published: (2026)

ChaosMining: A Benchmark to Evaluate Post-Hoc Local Attribution Methods in Low SNR Environments
by: Shi, Ge, et al.
Published: (2024)

CalArena: A Large-Scale Post-Hoc Calibration Benchmark
by: Berta, Eugène, et al.
Published: (2026)

Systematic Vulnerability Audit of Post-Hoc XAI under Common Corruptions: Analysis Pipeline
by: Kim, Minyeong
Published: (2026)

Post Hoc Regression Refinement via Pairwise Rankings
by: Wijaya, Kevin Tirta, et al.
Published: (2025)

XAI-on-RAN: Explainable, AI-native, and GPU-Accelerated RAN Towards 6G
by: Basaran, Osman Tugay, et al.
Published: (2025)

Human-Centered Evaluation of XAI Methods
by: Dawoud, Karam, et al.
Published: (2023)

Explainability Through Human-Centric Design for XAI in Lung Cancer Detection
by: Rafferty, Amy, et al.
Published: (2025)

Robust Taylor-Lagrange Control for Safety-Critical Systems
by: Xiao, Wei, et al.
Published: (2026)

OpenXAI: Towards a Transparent Evaluation of Model Explanations
by: Agarwal, Chirag, et al.
Published: (2022)

Toward Explainable Offline RL: Analyzing Representations in Intrinsically Motivated Decision Transformers
by: Guiducci, Leonardo, et al.
Published: (2025)

Seamful XAI: Operationalizing Seamful Design in Explainable AI
by: Ehsan, Upol, et al.
Published: (2022)

Explainable Generative AI (GenXAI): A Survey, Conceptualization, and Research Agenda
by: Schneider, Johannes
Published: (2024)

Explainable AI: XAI-Guided Context-Aware Data Augmentation
by: Mersha, Melkamu Abay, et al.
Published: (2025)

Explainable but Vulnerable: Adversarial Attacks on XAI Explanation in Cybersecurity Applications
by: Mia, Maraz, et al.
Published: (2025)

Guidelines For The Choice Of The Baseline in XAI Attribution Methods
by: Morasso, Cristian, et al.
Published: (2025)

Post-Hoc Reversal: Are We Selecting Models Prematurely?
by: Ranjan, Rishabh, et al.
Published: (2024)

Explainable AI: A Combined XAI Framework for Explaining Brain Tumour Detection Models
by: McGonagle, Patrick, et al.
Published: (2026)

Towards an Evaluation Framework for Explainable Artificial Intelligence Systems for Health and Well-being
by: Amengual-Alcover, Esperança, et al.
Published: (2025)

Agentic Explainability at Scale: Between Corporate Fears and XAI Needs
by: Elsayed, Yomna, et al.
Published: (2026)

TaylorPODA: A Taylor Expansion-Based Method to Improve Post-Hoc Attributions for Opaque Models
by: Tang, Yuchi, et al.
Published: (2025)

XAI-CF -- Examining the Role of Explainable Artificial Intelligence in Cyber Forensics
by: Alam, Shahid, et al.
Published: (2024)

Towards Quantitative Evaluation of Explainable AI Methods for Deepfake Detection
by: Tsigos, Konstantinos, et al.
Published: (2024)

Explainable Artificial Intelligent (XAI) for Predicting Asphalt Concrete Stiffness and Rutting Resistance: Integrating Bailey's Aggregate Gradation Method
by: Kongkitkul, Warat, et al.
Published: (2024)

False Sense of Security in Explainable Artificial Intelligence (XAI)
by: Chung, Neo Christopher, et al.
Published: (2024)

TumorXAI: Self-Supervised Deep Learning Framework for Explainable Brain MRI Tumor Classification
by: Zahin, Abrar Hossain, et al.
Published: (2026)

Rules or Weights? Comparing User Understanding of Explainable AI Techniques with the Cognitive XAI-Adaptive Model
by: Rawshan, Louth Bin, et al.
Published: (2026)