Latest Articles for Explainable AI

A clear guide to understanding feature importance and influence in AI decision-making.

This article discusses how to improve understanding of AI in music creation.

A new tool empowers users to understand AI decisions through counterfactual explanations.

Understanding how AI improves clinical risk prediction and trust.

Grad-CAM helps doctors trust AI in diagnosing medical conditions.

A new approach improves understanding of AI decisions in federated learning.

AdvisingNet enhances AI accuracy in image classification using nearest-neighbor examples.

Explore how sample size impacts AI explanations and transparency in decision-making.

A look at how counterfactuals clarify AI decision-making.

Learn how data can be turned into understandable text for everyone.

Understanding preferences for job recommendation explanations is vital for effective recruitment.

IA-ViT improves explanation quality in visual tasks.

A new framework aims to clarify AI decision-making for humans.

Introducing XFedHunter for advanced detection and analysis of APT attacks in networks.

Identifying risks and establishing requirements for safe AI use in healthcare.

Researchers use AI techniques to predict protein functions from amino acid sequences.

HuntGPT improves threat detection with a user-friendly interface and explainable AI.

New methods are developed to enhance attack detection in IoT devices.

Learn how AI helps farmers identify plant diseases quickly and accurately.

KAXAI makes machine learning easy and accessible for users of all backgrounds.

Improving methods for detecting abnormal Bitcoin transactions to enhance system security.

SCoBots improve reinforcement learning by enhancing object relationship understanding.

Exploring methods to make AI in finance more understandable and trustworthy.

New method improves understanding of AI decision-making processes.

SIDU-TXT sheds light on AI decisions in natural language processing.

SyntaxShap enhances understanding of language model predictions through syntax analysis.

This article discusses methods to certify explanations in machine learning models.

A study on using DistilBERT for effective phishing email detection.

Research shows age and openness impact understanding AI explanations.

Exploring methods to improve the reliability of AI decision-making explanations.

XpertAI offers tailored insights into AI predictions for better decision-making.

Exploring why AI model explanations are essential for trust and reliability.

Understanding self-driving cars is key to trust and safety.

This article examines how adversarial attacks alter the learned concepts of CNNs.

A study on the explainability of machine learning models for pancreatic cancer care.

SHIELD Regularization enhances AI performance and explainability for better understanding of decisions.

Exploring the relationship between anomaly detection, privacy, and explainability.

MCPNet offers clearer insights into deep learning model decisions through multi-level concept extraction.

Exploring the need for explainable AI in generative systems.

Introducing CAGE, a method that incorporates causality for clearer AI explanations.