To achieve explainable AI, they should keep tabs on the data used in models, strike a balance between accuracy and explainability, focus on the end user and develop key performance indicators (KPIs) to assess AI risk.

How does explainable AI work?

Explainable AI (XAI) is an emerging field in machine learning that aims to address how black box decisions of AI systems are made. This area inspects and tries to understand the steps and models involved in making decisions.

What is explainable AI example?

Examples include machine translation using recurrent neural networks, and image classification using a convolutional neural network. Research published by Google DeepMind has sparked interest in reinforcement learning.