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Explained: Explainable AI

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Roman Spitzbart
Roman Spitzbart
Roman Spitzbart, is the VP EMEA Solutions Engineering at Dynatrace.

Demystify AI and gain trust in its decisions. Learn how Explainable AI empowers businesses with clear, actionable insights for better decision-making and regulatory compliance.

What is Explainable AI (XAI)?

Explainable AI aims to make artificial intelligence more understandable and clear. This clarity will help teams have confidence and trust in the AI model, watching it perform complex tasks and make decisions while users audit any errors or concerns. 

Explainable AI is a rapidly changing area of AI technology development used in large organizations through Dynatrace Davis AI. This is a great example of a robust AI-powered platform utilizing Explainable AI methodologies. Such technology importantly streamlines and improves business capabilities. Ultimately, having a clearer picture of AI means better troubleshooting and even more opportunities to fine-tune organizations’ tools. 

What are the key principles of Explainable AI?

Different approaches to Explainable AI favor certain principles. Any comprehensive Explainable AI approach must consider interpretability, communication methods, and global vs. local understandability. First and foremost, it is crucial that any AI model can be understood, especially in terms of predictions and decisions. The depth of interpretability needed will depend on the AI model, but these predictions and decisions should be traceable through the model’s decision-making process. 

The communication methods used by an Explainable AI-oriented offering are also vital. Two of the most common visualization methods are decision trees and dashboards. These tools present complex data in an easily readable format that can be turned into actionable insights. Third and finally, global and local explanations play an important role. Global explanations give users insight into how the model acts, whereas local explanations are insights into an AI model’s individual decisions. Understanding global and local explanations allows organizations to have transparent information. 

Why is Explainable AI important?

Irrespective of the application and sector in which an organization operates, Explainable AI is only growing in importance. It is a critical part of business operations and industry standards, much more than just a matter of convenience. As more AI-powered technologies are introduced, more industry and government regulations will be enacted. This is a big reason why Explainable AI is so important, as explainability requirements will continue developing alongside artificial intelligence’s growth.

Also Read: Explained: Big Data

For instance, the UAE was the first country to appoint a Minister of AI. Following this, the UAE issued an official AI Ethics Guide to promote the conversation around AI ethics and help AI users adopt its core principles. Examples like these reinforce the importance of Explainable AI as a crucial aspect of industry standards and business operations, which organizations must adhere to as AI technologies continue to grow.

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