Large language models (LLMs) such as GPT and Llama are driving exceptional innovations in AI, but research aimed at improving their explainability and reliability is constrained by massive resource ...

Traditional rule-based systems, once sufficient for detecting simple patterns of fraud, have been overwhelmed by the scale, ...

Overview: Interpretability tools make machine learning models more transparent by displaying how each feature influences ...

Explainable AI provides human users with tools to understand the output of machine learning algorithms. One of these tools, feature attributions, enables users to know the contribution of each feature ...

From a governance perspective, the use of explainable AI is particularly significant. Infrastructure decisions involve public ...

When AI falters, it’s easy to blame the model. People assume the algorithm got it wrong or that the technology can’t be trusted. But here’s what I've learned after years of building AI systems at ...

AI decisions are only defensible when the reasoning behind them is visible, traceable, and auditable. “Explainable AI” delivers that visibility, turning black-box outputs into documented logic that ...

As I sat down with Jim Wilson, global managing director of thought leadership and technology at Accenture and co-author of the newly updated book Human + Machine: Reimagining Work in the Age of AI, ...

Over the past decades, computer scientists have developed many computational tools that can analyze and interpret images. These tools have proved useful for a broad range of applications, including ...