Explainable AI (XAI) - What is explainable artificial intelligence?

What will happen when one day AI takes over every field and profession and replaces humans in many activities without them understanding what artificial intelligence is doing? How did it happen? And why did it produce such results and decisions? It's too risky, right? Perhaps that's why Explainable AI was born in the programming world. Let's learn the basic information surrounding this term together!

What is explainable AI (XAI)?

Explainable AI (XAI), also known as Explainable Artificial Intelligence. This is a set of methods and procedures programmed to help users understand and trust the results or outputs produced by algorithms. 
Explanatory AI is used to describe an AI model, its predicted impact, and its potential impacts. This helps explain the accuracy, transparency, and results of the model in the decision-making process for AI-powered situations. It is known that Explainable AI is important for an organization in building trust when bringing AI models and applying it to production. At the same time, it also helps an organization or business apply responsibly to the AI ​​development process.
However, as AI becomes more advanced, users will need to face the challenge of understanding and following how the algorithm generates recommendations. The entire computational process would be turned into what is often called a "black box" and impossible to interpret. These black boxes are created directly from the data. At that time, even many engineers or scientists, studying the data that created them, could hardly fully understand or explain exactly what was going on and how the AI ​​produced specific results.
So, there are many advantages to understanding how an AI tool produces specific results. And explainability can help us ensure that the system is working as expected.

How does Explainable AI work?

With Explainable AI, we can access the basic decision-making process of AI technology and make adjustments when necessary. It can help improve the user experience of any product or service by helping them trust that AI is making the right recommendations and decisions. 
So when do AI results give you enough confidence that you can trust them and how can AI fix problems and errors that arise? As AI becomes more advanced, ML (Machine learning) processes still need to be understood and controlled to ensure accurate results. 
Try comparing AI and XAI to clearly see the difference. What methods and techniques are used to transform AI into XAI and the difference between interpreting AI processes:

Compare AI and XAI

Do you know the difference between “regular” AI and XAI - Explainable AI? It is XAI's deployment of detailed techniques and methods to ensure that each decision made can be tracked and explained accurately, objectively and convincingly. Meanwhile, conventional AI often produces results using ML algorithms, but few people fully understand how the algorithm operates and produces results. This will be a major limitation and flaw in the process of testing the accuracy of results. This can lead to loss of control and many subsequent consequences.

Techniques of Explainable AI

There are three main approaches in establishing XAI techniques: prediction accuracy, traceability that addresses requirements, and decision understanding that addresses human needs.

Prediction accuracy

This is considered an important component closely related to the level of success of using AI in various fields. Predict by running simulations and comparing XAI output with results in the original data set, from which accuracy can be determined. Among them, the most common techniques used for this purpose are explaining predictions using ML algorithms, locally interpretable model explanation (LIME),...

Retrieval capabilities

Accessibility is the second most important factor. This is often achieved by limiting the way decisions are made by establishing a narrower scope for ML rules and features. 
An example of XAI's retrieval capabilities is DeepLIFT (Simply understood, these are important features of Deep Learning). That is, comparing the activation of each neuron with its reference neuron. Then shows a traceable link between each activated neuron. Or it can even show us the dependence between them.

Understand the decision (outcome)

This belongs to the human factor. Many people in the process of using it do not yet or even do not trust AI. However, we all know that to be able to work effectively with AI, we need to learn to trust it the way humans work with humans. To do so requires users to be trained in how to work in the AI ​​area so they can understand how and why the AI ​​makes such decisions.

Explainability in AI

Interpretation is the degree to which users and experts can understand the causes of an outcome rather than simply explaining what is difficult for humans to understand. Explainability is also seen as going a step further and looking at how AI generates results rather than just humans predicting.
Above is some basic information about Explainable AI for beginners. And you, what do you understand about Explainable AI? Leave a comment so we can discuss in depth.