Latest Articles for Explanations

Assessing the accuracy of neuron explanations in language models reveals significant flaws.

Addressing challenges in translating difficult terms through added explanations.

A new MLOps architecture integrates explanations to enhance trust and efficiency in ML models.

Exploring AI's role in analyzing mental health on social media.

A new method offers multiple reasons for image classifications, enhancing understanding and trust.

A new evaluation method enhances understanding of GNN predictions.

Examining how robust machine learning models impact explanation effectiveness.

DiffChest improves chest X-ray analysis with clear explanations and confounder identification.

Introducing a probabilistic approach to assess GNN explanations for better reliability.

A look into how memes can spread harmful messages online.

This article explores LLM confidence and user perceptions.

Understanding image classifiers is vital for trust and reliability in various fields.

Users seek clarity and transparency in search engine results.

New methods enhance the evaluation of AI model explanations.

Understanding AI predictions is essential for healthcare professionals.

New method improves GNN explainability using proxy graphs.

ACTER offers effective explanations for machine decision failures in reinforcement learning.

A look at the need for fairness and clear explanations in AI systems.

This research evaluates AI model confidence and explanation quality in noisy environments.

A new method improves phishing detection and user understanding.

A new model provides clearer explanations for object detection decisions.

DSEG-LIME enhances AI model explanations for better understanding and trust.

Exploring the importance of understandable reasoning in AI predictions.

KGExplainer enhances transparency in knowledge graph completion through meaningful explanations.

AI models are evolving to assist in medical questions, but challenges remain.

Clear communication builds trust in autonomous vehicles for all road users.

Exploring how AI influences subjective choices and trust in explanations.

Counterfactual reasoning enhances understanding of vulnerabilities in code.

Discover how HyperLTL model-checking enhances the security of software systems.

Study reveals insights on the balance between visual and textual inputs in VLMs.

New method improves explanations for graph neural networks with robust counterfactual witnesses.

A new approach for clearer GNN predictions using edge-focused subgraph explanations.

This paper discusses the need for explainability in AI text generation models.

A new framework enhances user understanding of AI decisions.

A new method enhances fact-checking accuracy and clarity.

A new method improves understanding of Graph Neural Networks' predictions.

AI tools improve glaucoma referrals and patient outcomes.

Study focuses on how robots can handle conflicts and communicate effectively with users.

Enhancing LLMs' ability to refine their code through self-debugging techniques.

A new method to assess neuron explanations in deep learning models.