explainability

The study introduces SHAP-RTL, a rendering layer that corrects the visualization of SHAP and LIME explanations for right-to-left languages, addressing issues like token sequence and script shaping while preserving original attribution values.

This paper reviews the key domains of robustness, operating design domains (ODD), and explainability to build trust in AI systems for railway applications, aiming to meet strict safety standards and unlock broader adoption.

This paper fine-tunes a large language model for a video streaming service to generate personalized, faithful, and harmless recommendation explanations, improving pass rates without compromising original performance.

The paper investigates using Large Language Models as post-hoc auditors to evaluate symbolic regression models for interpretability and medical plausibility, with clinician assessments showing comparative model rankings are more favorably perceived than term-level interpretations.

CARRE is a three-stage framework that combines retrieval-augmented generation, counterfactual scoring, and large language model reasoning to provide explainable churn prescriptions for customer retention actions, showing improved risk reduction over baselines.

John Schulman highlights research by Adam Karvonen and colleagues on using counterfactual simulatability as a metric to improve AI explanation quality. They developed a dataset and pipeline that trains models to generate better post-hoc explanations of their own behavior, showing generalization to held-out evaluations.

The paper proposes CRAFT, a data-centric method for fine-tuning small language models to enable pre-hoc explainability in AI-native 6G RAN, achieving higher accuracy and energy efficiency compared to existing methods like GRPO.

NVExplain introduces a model-agnostic framework for explaining time series forecasting by analyzing latent trajectories and semantic flow, using structure-preserving surrogates to generate human-readable explanations with competitive faithfulness and stability.

Virgil is an interactive system designed to help users discover and compare explainability tools for transformer-based language models through a curated knowledge base and unified interface.

This paper introduces G2I, a two-stage greedy framework that uses explainable GNN explanations to generate actionable intervention hypotheses, improving efficiency over existing mask-based counterfactual methods for applications in public health and social sciences.

The paper introduces MIL-BERT, an algorithm for classifying arbitrarily long texts by selecting relevant excerpts, achieving state-of-the-art results on multiple datasets with performance and explanatory guarantees.

EXPL-FR is a method that explains face recognition models by aligning vision-language embeddings to the recognition space, enabling label-free auditing of semantic attributes and model comparison without requiring architecture access.

This paper introduces an Interaction-based Prompt Sensitivity (IPS) metric to evaluate and explain prompt sensitivity in large language models by analyzing interactions. It applies IPS to 50 open-source LLMs, identifying factors like fine-tuning and model scale that reduce sensitivity through low-order interactions.

The paper proposes EvalXRL, a benchmark for evaluating Explainable Reinforcement Learning methods by using an LLM coding agent to diagnose and fix bugs in RL agents.

Introduces DECAF, a method that decomposes perturbation responses into evidence, contradiction, and fragility components, improving interpretability over raw response magnitude and achieving strong results across vision benchmarks.

This paper introduces Groundedness Drift, a score for black-box auditing of language model classifiers to detect backdoors using clean calibration data and explanatory outputs. It demonstrates higher detection performance across multiple attack families and datasets.

This paper proposes RelShap, a framework that incorporates relational constraints and data provenance into Shapley value computation, making explanations more faithful to the data-generating process. It is estimator-agnostic and composes with existing SHAP estimators while exploiting functional dependencies to reduce runtime.

Introduces the open-source project Semantica, which provides traceable decision-making basis and audit-support capabilities for AI Agents by building Context Graph / Knowledge Graph, suitable for scenarios with high explainability requirements such as finance and healthcare.

The author shares how a triage agent with higher accuracy than humans still failed adoption until they added plain-language explanations for each decision, concluding that legibility beats accuracy for building trust.

This paper builds a multi-scale stacking ensemble for credit risk scoring and audits LLM-generated explanations, finding that ranking gains are real but small while the narrative explanations are often unfaithful, with SHAP and LIME agreeing on important features but not their order or sign.