The Most Comprehensive Guide On Explainable AI (XAi) you will ever need.

Overview

• Explainable AI is a feature recently launched by the US Defense Advanced Research Projects Agency (DARPA).
• It is a tool developed by the software library toolkit for interpretable artificial intelligence.
• In May 2018, the researchers showed the first experiment that was working on the result of this research.

Introduction

I love artificial intelligence and I like to delve into it a lot in all or all aspects, and I do the follow-up every day to see what is new in this field. I made the latest update to me. I missed this title and this technology that is before you today, which is one of the newest and rarest technologies that have been worked on recently. In artificial intelligence, at the end of this era, I have been working on the development of this new technology, which is called interpretable artificial intelligence, which works to clarify a new concept, which is how to communicate information better and well to the ordinary person, meaning that it works to make the results more flexible than before. As it works on drawing new, easy-to-understand, and easy-to-flex plans to make the average person work to understand these results in a large and accurate way.

able of Contents

1. What is explainable AI?
2. Understand explainable AI.
3. How does explainable AI work?
4. What are the features of the XAI interface?
5. How to understand explainable AI?
6. How does XAI serve AI?
7. What are its advantages?
8. Implement explainable AI

Understand explainable AI.

First of all, we must understand what XAI is and why this technology is needed. Hence, AI algorithms often act as “black boxes” that come in and provide the output anyway to understand their inner workings. Where the goal of Xai is to make the rationale behind producing an algorithm that is understandable by the ordinary person who is not familiar with the subject, making him fully aware of that subject.

Hence we can assume that, for example, many AI algorithms are used deep learning in this matter, where the algorithms learn to identify patterns based on the data bloating and the data with large training data. Whereas, deep learning is a neural network approach that simulates the way the brain of normal human beings operates like ours. As with human thought processes, it can be difficult or impossible to determine the extent to which a deep learning algorithm has reached a prediction or decision.

Here, decisions about employment and financial services issues such as credit scores and loan approvals are important and worth explaining. However, no one is likely to be physically harmed (at least immediately) if one of those algorithms gives poor results. But there are many examples where the dire consequences are much more than that.

Hence, deep learning algorithms are increasingly important in health care application use cases such as cancer screening, where it is important for clinicians to understand the basis for algorithm diagnosis. A false negative can mean that the patient is not receiving life-saving treatment. A false positive, on the other hand, may result in a patient receiving expensive treatment when it is most needed and necessary. The level of explanation is essential for radiologists and oncologists seeking to take full advantage of the growing benefits of AI.

How does Explainable AI work?

First we define and understand what interpretable AI is. Here we will explain how these principles help determine the expected output from XAI, but they do not provide any guidance on how to reach this desired outcome. It may be easy and well to divide XAI into three categories:

Where these questions are asked to clarify more and more, and the questions range as follows:

Interpretable data: What data was entered in training the model? Why was this data chosen? How was fairness assessed? Has any effort been made to remove bias?

Interpretable predictions: What features of the model are activated or used to reach certain outputs?

Interpretable algorithms: What individual layers does the model consist of, and how do they lead to the output or prediction?

Looking at the neural network in particular, interpretable data is the only category that is easy to achieve — at least in principle. Most researchers, or in other words, research leaders put the most emphasis on achieving interpretable predictions and algorithms. There are two current ways of interpretation:

Proxy modeling: A different type is used where the models are clarified and simplified than using methods such as a decision tree to approximate the actual model. Since it is an approximation, it may differ from the actual model results.

Interpretation design: The forms are designed to be easy to explain. This approach risks reducing the predictive power or overall accuracy of the model.

What are the different types of Xai?

We can work on the classification of XAI under two types:

These models are simple and fast to implement. Algorithms consist of simple computations that ordinary humans can do themselves. Thus, these models explain, and humans can easily understand how these models arrive at a particular decision.

What are the features of the Xai interface?

Here the features of this interface include: XAI interfaces depict the output of different data points to explain the relationships between specific features and model predictions. Where users can observe the x and y values of different data points and understand their effect on the absolute error received from the color code to understand.

How does XAI serve AI?

When we saw that artificial intelligence is more widely used in our daily lives, from here we went to an important point, which is the ethics of artificial intelligence. However, the increasing complexity of advanced AI models and the lack of easiness raise doubts about these models. Without understanding them, humans cannot decide whether these AI models are socially useful, trustworthy, safe, and fair. Thus, AI models need to follow specific ethical guidelines. Gartner combines the ethics of artificial intelligence into five main components:

• Clarity and transparency
• Human-centered and socially beneficial
• Exhibition
• Safe and secure
• Responsible

One of Xai’s primary goals is to help AI models serve these five components. Where humans need a deep understanding of AI models to understand whether they follow these components or not. Humans cannot trust an AI model that does not know how that model works. By understanding how these models work, humans can decide whether AI models follow all five of these characteristics.

What are its advantages?

Here its advantages will be mentioned as Xai aims to explain how to come to specific decisions or recommendations. From there, he explains and helps humans understand why AI behaves in certain ways and builds trust between human models and AI. The important advantages of Xai accumulate in:

• It improves explanation and transparency: Companies can understand organizational models, better understand developments, and see why they behave in certain ways under certain conditions. Even if it’s a black model, humans can use an interface of interpretation to understand how these AI models achieve certain conclusions.
• Faster adoption: As companies can better understand AI models, they can be trusted with more important decisions
• Debugging optimization: When the system is running unexpectedly, Xai can be used to identify the problem and help developers debug the problem.
• Enable audit for regulatory requirements

Mohamed B Mahmoud. Data Scientist.