Explainable AI

AI Safety

Chain-of-Thought Reasoning: When AI Explanations Deceive

New research reveals that AI models' step-by-step reasoning often doesn't reflect their actual decision process, raising critical questions about trust, safety, and the reliability of AI systems used for content authentication.

AI detection

Why AI Text Detectors Fail: Explainable AI Reveals Hidden Flaws

New research uses explainable AI techniques to reveal why AI-generated text detectors fail in practice despite strong benchmark scores, exposing critical shortcomings in current detection approaches.

mechanistic interpretability

Inside the Black Box: The Quest to Decode Neural Networks

Researchers are racing to understand what happens inside neural networks. Mechanistic interpretability could reshape how we build, audit, and trust AI systems — from deepfake detectors to video generators.

Research

SCE-LITE-HQ: Smoother Visual Counterfactuals via Diffusion

New research combines StyleGAN and diffusion models to generate high-quality visual counterfactual explanations, advancing explainable AI while revealing techniques applicable to synthetic media generation.

Explainable AI

FAME Brings Formal Verification to Neural Network Explanations

New research introduces FAME, a framework using formal methods to generate mathematically guaranteed minimal explanations for neural network decisions, advancing AI interpretability.

LLM safety

Explainable LLM Unlearning: Making AI Forget With Reasoning

New research introduces explainable approaches to LLM unlearning, enabling models to selectively forget information while providing transparent reasoning for the process.

Explainable AI

Building Explainable AI Pipelines with SHAP-IQ

Learn how to implement SHAP-IQ for understanding feature importance and interaction effects in AI models, enabling transparent decision breakdowns essential for trustworthy systems.

AI Interpretability

The AI Interpretability Crisis: What Black-Box Models Cost Us

Modern AI systems achieve remarkable results but remain fundamentally opaque. The interpretability crisis threatens trust, safety, and accountability across all AI applications.

AI Security

Adversarial AI Explanations: How Attackers Exploit Trust

New research reveals how adversarial attacks can manipulate AI explanation systems to mislead human decision-makers, with critical implications for content authenticity verification.

LLM Interpretability

LLM Self-Explanations Can Predict Model Behavior, Study Finds

New research presents evidence that LLM self-explanations can help predict model behavior, offering a positive case for faithfulness in AI interpretability.

Explainable AI

xDNN(ASP): Logic-Based Explainability for Neural Networks

New research combines deep neural networks with Answer Set Programming to generate human-readable explanations for AI decisions, advancing interpretability crucial for detection systems.

Explainable AI

New Framework Explains Why AI Generates What It Does

Researchers introduce prompt-counterfactual explanations, a new method for understanding generative AI behavior by identifying minimal prompt changes that alter outputs.