What is explainable AI, or XAI?
Explainable AI FAQs
What is meant by explainable AI?
Explainable AI is a set of processes and methods that allow users to understand and trust the results and output created by AI/ML algorithms. The explanations accompanying AI/ML output may target users, operators, or developers and are intended to address concerns and challenges ranging from user adoption to governance and systems development.
What is an explainable AI model?
An explainable AI model is one with characteristics or properties that facilitate transparency, ease of understanding, and an ability to question or query AI outputs.
Why is explainable AI important?
Because explainable AI details the rationale for an AI system’s outputs, it enables the understanding, governance, and trust that people must have to deploy AI systems and have confidence in their outputs and outcomes. Without XAI to help build trust and confidence, people are unlikely to broadly deploy or benefit from the technology.
What are the benefits of explainable AI?
There are many benefits to explainable AI. They relate to informed decision-making, reduced risk, increased AI confidence and adoption, better governance, more rapid system improvement, and the overall evolution and utility of AI in the world.
Does explainable AI exist?
Yes, though it’s in a nascent form due to still-evolving definitions. While it’s more difficult to implement XAI on complex or blended AI/ML models with a large number of features or phases, XAI is quickly finding its way into products and services to build trust with users and to help expedite development.
What is explainability in deep learning?
Deep learning is sometimes considered a “black box,” which means that it can be difficult to understand the behavior of the deep-learning model and how it reaches its decisions. Explainability seeks to facilitate deep-learning explanations. There is ongoing research in evaluating different explanation methods.
What explainable AI features does Juniper offer?
XAI can come in many forms. For example, Juniper offers blogs and videos that describe the ML algorithms used in several AIOps capabilities such as performing automatic radio resource management (RRM) in Wi-Fi networks or detecting a faulty network cable (see the VIDEO resources below). Some of these XAI tools are available from the Mist product interface, which you can demo in our self-service tour. Sign up here to get access today.