The Journey to Explainable Artificial Intelligence (XAI) Making AI Understandable

Explainable Artificial Intelligence (XAI)

With the advent of AI, there is growing concern about its transparency and fairness. In every sector, from generating billing lists to managing work, AI covered almost every field. Should we really depend on AI? Is AI-generated output error-free? To check the authoritativeness of AI, Explainable Artificial Intelligence (XAI) comes in. These XAI models comprehend the core of the AI model’s decision-making in order to improve trust in AI.

What is Explainable Artificial Intelligence (XAI)?

Explainable Artificial Intelligence (XAI) is a kind of process or method that helps humans understand the result generated by the machine learning algorithm. The main objective of AI is to check the accuracy, fairness, and transparency in AI decision-making. Apart from this, it also produces models that provide insightful learning performance. Overall, it evaluates, justifies, and improves AI.

Exploring XAI Techniques – 3 Popular Methods

Explainable AI uses three techniques to interpret AI models; let’s see what they are.

  1. LIME (Local Interpretable Model-Agnostic Explanations): It is a popular Explainable AI technique that helps users by providing them with local explanations for any learning model. The working model follows a two-step process: Sampling and Feature selection from the Surrogate Dataset.
  2. Rule-Based Models: Logical rules are easier to understand; that’s what a rule-based system does. They comprehend AI decisions in the form of logic. This logic can be easily understood by humans.
  3. SHAP: It depicts Shapley Additive Explanations. This technique is mostly used to interpret the prediction for any observation. SHAP provides an array of visualization tools that can be used to interpret models.

How do you Evaluate XAI Methods?

XAI methods are highly effective to check the suitability and accuracy of AI algorithms. Let’s check how anyone can easily evaluate Explainable Artificial Intelligence (XAI) methods.

  • Computational Efficiency: An effective XAI method must have computational efficiency. They are capable of providing accurate explanations within the given timeframe.
  • Interpretability: Users can define and interpret all the explanations generated by explainable XAI methods. These include sufficiency, fidelity, coherence, and faithfulness. 
  • Manual Evaluation Methods: By conducting different surveys or questionnaires, one can easily find out how effective XAI methods are. 
  • Scalability: XAI methods must be capable of running different tasks and producing effective results at the same time with fewer adjustments.

Advantages of Explainable Artificial Intelligence (XAI)

XAI comes with so many advantages; let’s explore them one by one.

  • Boost Productivity: With the help of explainable AI (XAI), it’s easier to understand errors. By understanding the loopholes or errors in the system, the technical team works to improve them so that the model works smoother for future predictions.
  • Improved Model Understanding: Explainable Artificial Intelligence (XAI) techniques are very useful in the understanding of AI-decision making models. As a result, the user gets a detailed understanding of the working model of AI and how it affects the outcome results.
  • Establish Credible Relations among Customers: With the assistance of explainability AI (XAI), users can easily ensure the accuracy of AI models. This further helps users to understand the working models behind AI, which boosts trust among them.
  • Reduce Risks: When a user or a company understand AI decisions, they easily comply with associated law and regulations, which further reduce error risks. 
  • Assured Business Value: When a user understands how AI operates, how it can assist business processes. They can be assured about the desired outcome and convinced to implement their business strategy. 

What are the four principles of XAI?

Explainable Artificial Intelligence (XAI) is mainly used to help users to understand the complexity of AI algorithms in an easy way. It works on four basic principles –

  1. Explanation: This implies that the system only delivers information, evidence, or reason for the generated output.
  2. Meaningful: Explainable AI provides meaningful explanations that are easier to comprehend for users.
  3. Explanation Accuracy: Some users are not satisfied with the simple explanations, as they need accurate explanations. When a user gets a proper explanation of the output, they are more willing to trust the accuracy of the AI algorithm. 
  4. Knowledge Limits: This principle mainly focuses on the knowledge limit of the systems. It finds out those conditions or cases, where the AI algorithm is not reliable or unable to produce desired results. By knowing the knowledge limits, users find more trust in AI-decision making models.

What is the difference between AI and XAI?

Artificial Intelligence (AI) and Explainable Artificial Intelligence (XAI) both look similar but are different concepts. Let’s find out what makes them different.

Aritificial Intelligence (AI)

  • It is a computer system or a software type, it helps users to generate outputs from queries.
  • It mimics human cognitive abilities.
  • Sometimes, it generates wrong information about the queries.
  • It helps businesses to automate all their workflows.

Explainable AI (XAI)

  • It’s an AI-focused based model, that evaluates the transparency of AI models.
  • It provides clear explanations about the AI outputs, which further build trust among users or customers.
  • It finds AI limitations so that users get to know about conditions where AI is unable to work. Overall, it saves a lot of time.
  • It is mainly programmed to deliver easier explanations about the AI models.


Every industry incorporates AI in its business strategies to make it more effective and profitable. To monitor the accuracy of AI or machine learning, you must have to consider using Explainable Artificial Intelligence (XAI). This article is packed with complete knowledge of explainable AI (XAI) and how it works. I hope it resolves all your queries. 

FAQ About Explainable Artificial Intelligence (XAI)

Ques 1. What is the concept of XAI?

Ans. The concept of explainable AI (XAI) is incorporating methods or techniques that assist humans in grasping outputs produced by AI or machine learning algorithms.

Ques 2. What are the different types of XAI?

Ans. There are three types of explainable AI:

  • Interpretable AI
  • Interactable AI
  • Transparent AI

Ques 3. What are the four principles of XAI?

Ans. Explainable artificial intelligence has four principles.

  • Explanation
  • Meaningful
  • Explanation Accuracy
  • Knowledge Limits

Ques 4. Why is explainable AI (XAI) important?

Ans. We don’t know about the authenticity of AI-generated outputs. Maybe they generate the wrong output, which misleads the process. To check its transparency and accuracy, XAI is crucial.

Ques 5. What are the uses of XAI?

Ans. Explainable AI (XAI) assists users to understand and interpret the complexity of the outputs generated by artificial intelligence (AI).

We hope this article will be helpful to you. Stay tuned for upcoming articles.

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