Explainable Artificial Intelligence (XAI)

Derniers papiers

• CoWeaver: A Bi-directional, Learnable and Explainable Matching Engine for Mixed Human-Agent Science Collaboration

  Jiayao Gu, Kexin Chu, Peidong Liu, Yue Yang, Lynn Ai, Qi Zhang, Ling Yang, Tianyu Shi · 20 juillet 2026

  LLM-based agents excel at writing articles, coding and information retrieval. However, they fail to form strong collaborations within the scientific community due to the bidirectional, dynamic nature of the problem and a high demand of decision interpretability. We proposed COWEAVER, a bidirectional…

• Recursive Harness Self-Improvement

  Hyunin Lee, Jinglue Xu, Jeffrey Seely, Donghyun Lee, Matei Zaharia, Yujin Tang · 20 juillet 2026

  Under model--harness co-evolution, harnesses are not merely inference-time scaffolds but data-generating components whose execution traces can shape future foundation models. This motivates harness-in-the-loop learning: optimizing harnesses for both immediate agent performance and the quality of tra…

• Inpainting Insights: Elevating Visual XAI with Photorealistic Perturbations

  Josef Lindl, Mariana Chaves, Damien Garreau · 20 juillet 2026

  The increasing complexity of state-of-the-art machine learning models has made their behavior progressively harder to interpret, spurring rapid advancements in the field of eXplainable Artificial Intelligence (XAI). Among many methods proposed, perturbation-based approaches play a major role. By sys…

• From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems

  Eduardo C. Garrido-Merch\'an · 20 juillet 2026

  A trained deep reinforcement learning policy is a black box, and we ask whether it can be made explainable by rewriting it as an executable logic program that reproduces its behaviour and that a person can read, a logic engine can run, and an optimizer can edit. We present a three-stage post-hoc tra…

• qZACH-ViT: Quantization-Aware Intrinsic Explanations with Recursive Attribution-Stabilized Optimization

  Athanasios Angelakis · 20 juillet 2026

  Compact medical-image classifiers need efficiency and interpretable evidence, yet these goals are often addressed separately. We introduce qZACH-ViT, a quantization-aware extension of the zero-token (CLS-token-free), position-free ZACH-ViT backbone with recursive intrinsic patch-level class evidence…

• Aggregation of Statistical Evidence under Exchangeability

  Antonin Schrab, Rajen Shah, Arthur Gretton, Ilmun Kim · 20 juillet 2026

  We study aggregation of statistical evidence under unknown and potentially complex dependence using group-invariance. Building on permutation-based constructions that treat transformed datasets as exchangeable units, we aggregate evidence across statistics for each transformed dataset and calibrate …

• DSWorld: A Data Science World Model for Efficient Autonomous Agents

  Zherui Yang, Fan Liu, Hao Liu · 20 juillet 2026

  Despite strong capabilities in data understanding and decision-making, autonomous data science agents still heavily rely on trial-and-error workflows that involve expensive computation. This bottleneck motivates models that can anticipate the effects of data science operations before real execution.…

• Neuro-Symbolic AI for LEED compliance: Document-Centric Benchmarking, Deterministic Numeric Checking, and When Multimodal Hurts

  Aritro De (The University of Texas at Austin), Juliana Felkner (The University of Texas at Austin) · 20 juillet 2026

  LEED v4.1 BD+C certification remains a document-intensive process that requires reviewers to read hundreds of pages of project evidence and apply credit-specific threshold logic by hand. This paper investigates whether small, locally deployed language models can perform meaningful screening of LEED …

• Towards a Unified Multidimensional Explainability Metric: Evaluating Trustworthiness in AI Models

  Georgios Makridis, Georgios Fatouros, Athanasios Kiourtis, Dimitrios Kotios, Vasileios Koukos, Dimosthenis Kyriazis, Jonh Soldatos · 17 juillet 2026

  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. Our methodology focuses on thr…

• Counterfactuals for Feature-Weighted Clustering

  Richard J. Fawley, Renato Cordeiro de Amorim · 17 juillet 2026

  Counterfactual explanations provide local, interpretable insight by identifying changes to an input that would alter its assigned outcome. Although well established in supervised learning, their extension to clustering is less direct, since cluster assignments are unlabeled and governed by the geome…

• Position: Explainability Research Must Prioritize Foundations over Ad-hoc Methods

  Michal Moshkovitz, Suraj Srinivas, Lesia Semenova, Nave Frost, Cyrus Rashtchian, Valentyn Boreiko, Shichang Zhang, Himabindu Lakkaraju, Cynthia Rudin, Jennifer Wortman Vaughan · 17 juillet 2026

  Despite the proliferation of Explainable AI (XAI) techniques -- from feature attributions to sparse autoencoders -- explanations rarely influence real-world workflows. In practice, they are often generated and discarded without guiding meaningful action. This gap reflects foundational shortcomings: …

• Local Additive Feature Attribution: A Mathematical Taxonomy and Reporting Checklist

  Rebecca Afriyie Sarpong, Daniel Commey · 17 juillet 2026

  Feature-attribution methods are central to explainable artificial intelligence. Their assumptions are expressed in several mathematical languages: cooperative-game values, path integrals, gradient operators, perturbation distributions, and backpropagation rules. This survey proposes a common framewo…

• Counterfactual Optimal Action Trees (COAT): Interpretable Prescriptive Policies from Observational Data

  Youssef Drissi, Markus Ettl, Shivaram Subramanian, Wei Sun, Zack Xue · 17 juillet 2026

  We introduce COAT (Counterfactual Optimal Action Tree), a framework for learning interpretable prescriptive policies from observational data. COAT combines counterfactual outcome estimation with large-scale mixed-integer optimization, using column generation to translate causal predictions into feas…

• AI-accelerated End-to-End Framework for Rapid Professional Upskilling

  Tam Nguyen, Hung Nguyen, Robert Ogburn · 16 juillet 2026

  By 2030, 59 of every 100 workers will need reskilling or upskilling, yet the average time to close an enterprise skills gap grew from roughly 3 days in 2014 to 36 days in 2018. Most current frameworks accelerate single stages of upskilling programs and generally lack industry validation. We present …

• Federated Explainable Artificial Intelligence: Roles, Architectures, Evaluation, and Open Challenges

  Masoume Gholizade, Fabrizio Ruffini, Pietro Ducange, Francesco Marcelloni · 16 juillet 2026

  Federated Learning (FL) has emerged as a key paradigm for privacy-preserving collaborative model training across distributed and heterogeneous data sources. By keeping raw data local, FL addresses data confidentiality concerns, yet it does not resolve the opacity of modern machine learning models. I…

• From Observation to Insight: Mechanistic World Models and the Quest for Autonomous Discovery

  Ingmar Posner, Anson Lei, Bernhard Sch\"olkopf · 16 juillet 2026

  Recent advances in foundation models have transformed AI for Science, enabling remarkably accurate predictive performance across domains ranging from protein folding to weather forecasting. Yet prediction alone does not constitute scientific discovery. Scientific understanding depends on uncovering …

• AI advice suppresses people's willingness to say "I don't know", even when the advice is wrong and accuracy is incentivized

  Chiara Marcoccia, Walter Quattrociocchi, Valerio Capraro · 16 juillet 2026

  Knowing when to say "I don't know" is fundamental to human judgment, yet AI assistants offer a fluent answer to almost any question. In five experiments (N = 3,132; four preregistered, one direct replication), participants answered difficult questions and could always decline to respond. We engineer…

• Explainable Artificial Intelligence for Anomaly Detection in Banking Transactions: An Internal Audit Perspective

  Anupa Lodhi · 16 juillet 2026

  The banking sector increasingly relies on automated systems to monitor electronic transactions for signs of fraud, yet conventional rule-based approaches struggle with high false-positive rates and offer no justification for their outputs, limiting their utility for compliance teams. This paper intr…

• Explaining Reinforcement Learning Agents via Inductive Logic Programming

  Celeste Veronese, Edoardo Zorzi, Daniele Meli, Alessandro Farinelli · 16 juillet 2026

  Explainable Reinforcement Learning (XRL) seeks to make Reinforcement Learning (RL) policies more transparent and interpretable, a key requirement in safety-critical and human-centric scenarios. However, it is mostly based on user studies, thus targeting the needs of a specific audience and lacking s…

• Relevance-Aware Rule: Structural Deletion of Irrelevant Conditions in Decision Trees

  Jung-Sik Hong, Jeongeon Lee, Min Kyu Sim, Sangheum Hwang · 16 juillet 2026

  Decision trees generate interpretable if--then rules, yet they contain irrelevant conditions (IRCs). These IRCs arise from the structural mechanism of tree splitting and persist even in modern optimal sparse tree induction algorithms. Existing IRC deletion methods overlook this structural mechanism;…

• AIMO Interpretability Challenge

  Michal \v{S}tef\'anik, Philipp Mondorf, Andreas Waldis, Qianying Liu, Chuan Yang, Michal Spiegel, Josef Kucha\v{r}, Marek Kadl\v{c}\'ik, Adam Vawda-Oomerjee, Chaoran Liu, Simon Frieder, Barbara Plank, Fazl Barez, Pontus Stenetorp · 16 juillet 2026

  We propose the AIMO Interpretability Challenge, a competition on distinguishing robust from spurious reasoning in frontier mathematical language models based on the models' internal mechanisms. The challenge is motivated by a central limitation of standard reasoning benchmarks: strong final-answer a…

• Is the Statistical Advantage Worth the Cost? An Empirical Comparison of KANs and MLPs for Structured Data Classification

  Matthew Steven P. Toledo, Justine Raphael H. Jacinto, Vivekjeet Singh Chambal, Rodolfo C. Camaclang III, Jamlech Iram N. Gojo Cruz, Reginald Neil C. Recario · 16 juillet 2026

  This study presents an empirical benchmarking comparison between Kolmogorov-Arnold Networks (KANs) and Multi-Layer Perceptrons (MLPs) on structured tabular classification tasks. Motivated by the growing interest in KANs as an alternative function-approximating architecture, we evaluate their out-of-…

• STOCKTAKE: Measuring the Gap Between Perception and Action in LLM Agents with a Fair Oracle

  Sagar Deb, Ashwanth Krishnan · 16 juillet 2026

  LLM agents are increasingly evaluated on multi-week decision tasks in which the state that drives cost is never directly observed. On such tasks the final cost cannot say why an agent failed: it may have misread the world, or read it correctly and still failed to act (the knowing-doing gap). Existin…

• Gradient-Skipping Relevance Propagation for Efficient Explainability of Vision Transformers

  Christopher Buratti, Michele Marchetti, Federica Parlapiano, Davide Traini, Domenico Ursino, Luca Virgili · 14 juillet 2026

  Vision Transformers (ViTs) are difficult to interpret because current methods of relevance propagation and attention flow do not fully consider some key architectural features, such as the uneven importance of attention heads and residual connections. Prior approaches typically assume uniform import…

• From Neural Network Decisions to Training Cases: An Exact Account via Case-Based Decision Theory

  Manli Yan, Yuebin Lin, Yaowen Yu, Yong Zhao · 14 juillet 2026

  Neural networks increasingly guide decisions in high-stakes domains such as medical diagnosis, credit approval, and energy bidding. Audit in these settings requires case-level evidence: which training cases support an action and what outcomes they carried. Case-based decision theory (CBDT) formalize…