Interpretable AI and Explanatory AI

As artificial intelligence (AI) continues to be integrated into various industries, there is a growing concern about the transparency and interpretability of AI algorithms. Interpretability and explainability are important aspects of AI, especially in highly regulated industries such as healthcare and finance. In this article, we will discuss the differences between interpretable AI and explanatory AI.

Interpretable AI refers to the ability of an AI model to provide understandable explanations or insights about its decision-making process. This approach involves creating models that can be easily understood by human experts, without requiring a deep understanding of complex mathematical algorithms. The focus of interpretable AI is to improve the transparency and trustworthiness of AI models by providing clear and concise explanations for their decisions.

Explanatory AI, on the other hand, is a more complex approach to AI that goes beyond just providing explanations. This approach involves creating models that can reason, communicate, and interact with humans in a natural and intuitive way. Explanatory AI systems are designed to provide more detailed and comprehensive explanations for their decisions, by highlighting important features, providing context, and explaining the underlying reasoning process.

The key difference between interpretable AI and explanatory AI lies in the level of detail and comprehensiveness of the explanations provided. Interpretable AI provides high-level explanations that can be easily understood by human experts, while explanatory AI provides more detailed and comprehensive explanations that can be understood by both experts and non-experts.

Interpretable AI has been gaining popularity in industries such as healthcare, where it is important to understand the underlying factors that contribute to a diagnosis or treatment recommendation. In these industries, interpretable AI is used to provide clear and concise explanations for the decisions made by AI models, which can help improve the accuracy and reliability of these models.

Explanatory AI, on the other hand, is more suited for industries such as finance, where it is important to understand the underlying factors that contribute to a decision. In these industries, explanatory AI is used to provide more detailed and comprehensive explanations for the decisions made by AI models, which can help improve the transparency and accountability of these models.

Overall, both interpretable AI and explanatory AI are important approaches to AI, and the choice between the two depends on the specific needs and requirements of the industry or application. While interpretable AI provides high-level explanations that are easy to understand, explanatory AI provides more detailed and comprehensive explanations that can be understood by both experts and non-experts. As AI continues to advance and become more integrated into various industries, the need for interpretable and explanatory AI will only continue to grow.

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