Explainable AI: When AI facilitates decision-making – LeBigData.fr

Explainable AI provides an understanding of the workings of artificial intelligence and its usefulness in various sectors. It sheds light on and accepts the use of AI by humans. More precisely, it helps to understand how a computer program arrived at such and such a decision.

Most companies use artificial intelligence to speed up decision-making. This effectively reduces costs, but also makes work easier. But when a bank uses AI to deny credit or when a company uses it to fire an employee, that’s where it all gets fuzzy. Solving such a problem involves what is called Explainable Artificial Intelligence or XAI. AI users need understand how the program works and how it evaluates results. Without this explanation, AI will become less important and less reliable for humans. To clarify the subject, we will discover what Explainable AI is, its importance, its objectives, the advantages it provides, etc.

What is Explainable AI?

Along with the development of AI, humans feel increasingly alienated from the decision-making process. Therefore, we need understand how the algorithm proceeded to achieve this or that result, hence the introduction of Explainable AI, XAI or eXplainable AI.

To put it simply, this notion refers to a principlea procedure by which a artificial intelligence pass to make a decision. The XAI simultaneously integrates the results obtained while indicating the way opted to achieve this. This approach is opposed to the principle of the black box where nothing can be interpreted even by the most gifted of scientists.

In other words, eXplainable AI encompasses human-understandable processes and methods as well as the ML algorithms to achieve the results. This aspect of AI achieves transparency and facilitates the traceability of a result. It also helps to detect potential biases.

When an organization wants to achieve results with precision and building trust with the help of AI, she uses such a method. In addition, this makes it possible to guard against any potential malfunction that could affect the decision to be taken or modify the final result.

Ordinary AI vs. XAI: what’s the difference?

Artificial Intelligence differs from Explainable AI in a number of ways. At first glance, using an AI, we always arrive at a result from a machine learning algorithm.

Only, we can’t explain how the algorithm operated to obtain such a result. Which means in a way that the AI ​​refers to the famous Black Box. Therefore, it is difficult to confirm the accuracy of the result. This will then lead to some loss of reliability of this same result.

On the other hand, XAI consists of the implementation of specific methods for tracing and explaining the path taken when making a decision. With this in mind, users can ensure that every decision made through the machine learning algorithm is unbiased. Otherwise, they will be able to make the decisions correcting this anomaly.

For what purpose?

Have faith in artificial intelligence, this is the ultimate goal of Explainable AI. Indeed, AI has become an essential element in the daily life of today’s society, whether professionally or privately. Today, many people are opting for smart devices like smartwatches to make their life easier. Similarly, applications used by government and business are equipped with AI.

This means that we can no longer do without new technology in almost all aspects of life. But for that, we must give him complete confidence. Many AI users have begun to tap into this direction.

Take, for example, the case of the Nvidia company in 2017. The firm published, through its developers, a blog post explaining how to drive a car autonomously using AI. The article highlighted research findings while unveiling the learning process of AI based on easy examples.

Some use cases

Explainable AI is now used in almost all fields, the most important of which are medicine, defence, the automotive industry, etc. Many medical institutions are opting for explainable AI in an effort to predict risk incurred by patients. This will sensitize them and allow them to choose interventions for preventive purposes.

As for the automotive industry, it has also introduced the method of explainable AI. Take for example the case of park-assist. This technology allows the driver to remain calm while the artificial intelligence on board the vehicle takes care of the parking lot. Indeed, without knowing how the machine acted to achieve this, the driver might be skeptical of the idea of ​​letting a machine take control of his vehicle and his life with it.

the different fields of application of the XAI

Why is eXplainable AI important?

Artificial intelligence is a great help for humans. However, it must be remembered that this is a fruit of our prowess over the years. Which means it can’t always be perfect. Sometimes the AI ​​shows flaws and comes with errors.

In view of what has been said, we cannot always blindly trust AI. Any decisions made in consideration of the results displayed by a machine are not not always reliable. Black box models are not enough to make a decision without knowing how and why to do so. This is the importance of Explainable AI because it accesses a full understanding of what is or should be.

Interpreting ML models is far from child’s play. Understanding the neural networks used in the ML process is not easy for a human. Certain criteria such as gender, age or race lead to biased results that are difficult to understand.

Moreover, the discrepancies between the different types of data can degrade the process, and therefore the result. Therefore, the company is required to control models at any time in order to have an overview of the impact of the use of XAI in the company. We can also mention the importance of Explainable AI when it comes toaudit and compliance within an organization. This will boost its visibility and reputation.

Finally, the adoption and responsible AI implementation relies heavily on Explainable AI. We are talking here about the large-scale implementation of AI techniques based on ethical and responsible principles.

The Benefits of Explainable AI

The importance of XAI translates into many advantages that we will see closely in the next paragraph. In fact, the adoption of explainable AI accesses 3 particular advantages:

Complete confidence in the exploitation of artificial intelligence

For a company, adopting an automated process can increase productivity. Having full confidence in AI allows you to produce more. The rapid introduction of AI models into production results from this climate of trust between human and machine.

In addition, when the organization manages to trust AI, it can work transparently and peacefully. The traceability of the results being ensured, there is nothing more to fear in the production process.

The man does not understand the modus operandi of the AI

Get results fast with AI

The AI ​​accesses a speed of the productive process. Nevertheless, it is necessary to choose systematic control and monitoring of models to ensure that there is no error or bias. A high-performance model provides satisfactory results. This implies the need for continuous evaluation of AI models.

Explainable AI reduces cost and risk

In a company, you have to opt for explainable AI models that prove to be transparent. This makes it possible to reduce the costs of management but also to minimise, if not eliminate, any risk linked to this management of the models. In this case, manual inspection, like errors, will no longer cause additional costs. The same is true for the unintended biases caused by human error.

The different methods for implementing XAI techniques

Since XAI provides so many specific advantages, its adoption is therefore a necessity. To do this, an effective method must be adopted. In this part, we will discover 4 methods by which we can implement explainable AI.

First, there is the Layer-wise Relevance Propagation or LRP. In French, it consists of a technology which makes it possible to define the specificities of the input vectors. The latter largely contribute to the output result of a neural network.

Second, we have what is called Counterfactual Method. This refers to the way in which we modify the data inputs once we have obtained a result. The observation then focuses on the conditions leading to the modification of the results.

After these two methods, we can also cite the LIME or Local Interpretable Model-Agnostic Explanations. This is an explanatory model based on a holistic approach. The explanation can relate to any mechanical classifier as well as the predictions which follow. With such a method, even the uninitiated can access data and methods. rationalization. This method only applies when dealing with AI-based bots such as the ChatGPT. Here, the machine is endowed with an autonomy that allows it to explain its actions.

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Explainable AI: When AI facilitates decision-making – LeBigData.fr

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