explainability

Introduces TELL, an AI-generated text detection system that provides explainable annotations alongside numerical scores, achieving competitive AUROC of 0.927 while enabling users to judge authorship based on highlighted textual indicators.

This paper introduces the Attribution Contract, a specification for feature-attribution claims in generative language models, addressing ambiguities in what constitutes a feature and how attribution methods should be evaluated. It uses autoregressive and diffusion models as case studies to show when attribution is informative or misleading.

The article argues that the primary challenge of AI in 2026 is not technical development but communicating probabilistic outputs to traditional stakeholders accustomed to deterministic guarantees, requiring skills in explanation and persuasion.

INSIGHTS is a model-agnostic approach for providing global explanations of time-series models by generating diverse, informative sample summaries that capture domain-specific behaviors, outperforming local attribution methods in user studies.

This paper proposes a pipeline for fine-tuning LLMs specifically for explainable misinformation detection and introduces LonsRex, a data synthesis method to generate necessary and sufficient rationales, addressing limitations of naive filtering based solely on label correctness.

This paper proposes GESD, a procedural-oriented fairness metric that measures disparities in explanation stability across subgroups, and integrates it into a multi-objective optimization framework for jointly optimizing utility, outcome fairness, and explanation fairness.

FairHealth is an open-source Python library designed for trustworthy healthcare AI in low-resource settings, offering modules for fairness auditing, privacy-preserving federated learning, and explainability.

The author details their decision to exclude LLMs from generating final fact-check verdicts in favor of a hybrid architecture that uses LLMs for data extraction and a deterministic Python layer for scoring, citing issues with stochastic instability and auditability.

This paper introduces Model-to-Data (M2D) distillation, a framework that transfers complexity from Graph Neural Networks to the data space to enhance architectural transparency and interpretability.

TriEx introduces a tri-view game-based framework that aligns self-reasoning, opponent belief states, and oracle audits to make multi-agent LLM decisions auditable and reveal mismatches between stated rationales and actual behavior.

Researchers from Peking University introduce CFMS, the first fine-grained Chinese multimodal sarcasm detection benchmark with 2,796 image-text pairs and a triple-level annotation framework (sarcasm identification, target recognition, explanation generation), along with a novel RL-augmented in-context learning method (PGDS) that significantly outperforms existing baselines.

A comprehensive survey reviewing recent advances in intrinsic interpretability for Large Language Models, categorizing approaches into five design paradigms: functional transparency, concept alignment, representational decomposability, explicit modularization, and latent sparsity induction. The paper addresses the challenge of building transparency directly into model architectures rather than relying on post-hoc explanation methods.

A comparative study evaluating three explainability techniques (Integrated Gradients, Attention Rollout, SHAP) on fine-tuned DistilBERT for sentiment classification, highlighting trade-offs between gradient-based, attention-based, and model-agnostic approaches for LLM interpretability.

OpenAI proposes using language models (GPT-4) to automatically generate and score explanations for neurons in language models, open-sourcing datasets and tools covering all 307,200 neurons in GPT-2. The work demonstrates iterative and scalable approaches to mechanistic interpretability, though explanation quality still lags behind humans.

OpenAI introduces Activation Atlases, a technique for visualizing and understanding the internal representations of neural networks, enabling humans to discover spurious correlations and unexpected behaviors such as fooling image classifiers by adding noodles to images.

This article discusses the importance of interpretability in artificial intelligence, focuses on chain-of-thought reasoning as a tool for understanding the inner workings of neural networks, and analyzes its current effectiveness, limitations, and the interpretability challenges that future more powerful models may bring.