In this article, we will explore in detail why XAI is important, how it works, the main techniques it comprises, and its application.
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What is Explainable Artificial Intelligence (XAI)?
Explainable Artificial Intelligence, or XAI , is a key advancement in the development of AI, especially in areas where transparency and trust are critical. Unlike traditional AI, which is often like a “black box” where we see the outcome but not the process, XAI allows users to understand how decisions are generated . Simply put, XAI focuses on giving clear explanations for how AI makes its decisions.
This approach not only helps build trust in AI, but also makes it useful in high-impact areas. In sectors such as healthcare, finance, marketing, and law, where decisions can directly affect people, understanding the reasons behind outcomes is crucial. With XAI, the decisions AI makes are justifiable, safe, and reviewable, making it easy to use in organizations that need high levels of transparency.
How does explainable artificial intelligence (XAI) work?
Using advanced techniques that “open up” the decision-making process, XAI enables users to understand both the “what” and the “why” behind each prediction or recommendation.
Traditional vs. XAI models
To understand how XAI works, it is first helpful to compare it to traditional AI models:
Traditional AI: “Black box” models that generate predictions without explaining the factors behind the results.
XAI: Uses various techniques that show the logic and variables that affect the results of a model, allowing users to understand the decision process.
Key difference: XAI allows you to see not just the “what,” but also the “why” and “how” of each outcome. This makes it ideal for applications where decisions need to be justifiable, such as marketing, where understanding what drives campaign performance is critical.
Explainability techniques in XAI
To achieve explainability, XAI uses several techniques, including:
