🎯 Developing explainable AI (XAI) agents: building trust and transparency
Making AI decisions understandable to users | Get started with AI | How-to guides and features
As AI agents become more sophisticated and integrated into critical applications, their decision-making processes often become opaque, leading to a lack of trust and potential ethical concerns.
Explainable AI (XAI) aims to address this by making AI models more transparent and understandable to human users.
This post explores the importance of XAI for virtual agents, particularly when they leverage diverse and complex datasets, and delves into the technical approaches for achieving greater transparency.
This is a practical guide to help decision-makers and board members navigate this evolving landscape.
We must grapple with fundamental questions about the nature of identity, consciousness, and the very essence of human existence.
This is a new sub-series of the Deep Dive series “How to build with AI agents.” which aims to help you proactively address potential issues and empower your IT and support agents with automation tools and AI for faster case resolution and insights.
It follows the series “How to build with AI agents. "This time, we focus on building on your existing foundation and the unique aspects of AI agents.