What is Explainable AI (XAI)? How can enterprises trust AI-based systems with decision-making?


As AI/ML models become ubiquitous across industries, there is also a need to understand the reasons for predictions and decisions. A new and emerging technology, the application of Artificial Intelligence is diverse, from conversational AI to autonomous vehicles. However, the machine learning models driving AI systems are hard to understand, leading to enterprises demanding accountability and trustworthiness of their AI systems.

Explainable AI helps industries benefit significantly by providing a deeper understanding of data, uncovering any bias in data, and further improving models and explaining how decisions are made. The AI guidelines call for AI systems to be transparent, safe, and trustworthy.

This thought paper aims to outline the definition of Explainable AI, the general landscape of Explainable AI in regulated and unregulated industries, and the key attributes of an ideal AI platform. Download the paper to learn how enterprises can use different techniques to address explainability requirements.

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