How Explainable AI can help overcome mistrust regarding the adoption of Artificial Intelligence

Artificial intelligence (AI) is one of the most promising technologies of our time. However, many people are still skeptical about its adoption because of the lack of transparency in decision-making processes. For this reason, Explainable AI (XAI) technology, which aims to make artificial intelligence decisions transparent, is becoming increasingly important. In this article, we will discuss how XAI can help overcome mistrust regarding the adoption of artificial intelligence and what its benefits are.

It is an emerging technology that aims to solve this problem by providing an explanation of how AI reached a particular decision. So let's go over how XAI can overcome misgivings about AI adoption and how it can help make this technology clearer. 

XAI has several advantages:

1. Transparency
   XAI aims to describe the AI decision-making process by making it more transparent. Lack of transparency is also a problem because it happens that AIs make decisions influenced by cognitive biases-biases about race, gender or religion etc-acquired through the huge amounts of data with which they are trained. Making these processes more limpid means that users can understand why the AI made a particular choice, and can check that the decision was made in the right way.
   
   
2. Trust
   XAI can increase people's trust in artificial intelligence as it contributes to the knowledge of how the AI gave a given output. In fact, users must be enabled to understand every suggestion they receive.
   
   
3. Reliability
   A transparent system also allows errors to be detected and corrected by improving the reliability of the AI. The explanations provided by the XAI can help developers identify any errors in the algorithm and correct them.

The XAI uses several techniques to provide an explanation of how the AI reached a particular decision. Some of these techniques include:

1. Association rules.
   Association rules identify relationships between different variables and determine how these relationships affect the AI's decision.

2. Probabilistic graphical models
   Probabilistic graphical models use probability theory to determine how different variables affect AI processes.

3. Interpretable neural networks
   Interpretable neural networks are an emerging technique in XAI. They are neural networks designed to explain artificial intelligence decision making. They are also designed to be easier to understand than traditional neural networks.

The XAI has numerous applications in various fields. Some of these applications include:

1. Health care
   XAI can help physicians make better and more informed decisions, such as identifying the causes of a disease or identifying the best treatments.

2. Information security.
   XAI is useful in identifying vulnerabilities in computer networks, potential hacker attacks, and improving the security of computer networks.

3. Finance
   XAI can identify financial risks and make better investment decisions. For example, XAI can help investment companies identify stocks that are most likely to succeed.

Despite the many benefits, there are also some limitations. Some of these include:

1. Complexity
   XAI can be very difficult to interpret for people who are not experts in the field.

2. Resources
   XAI takes time, money, and personnel, to develop and implement-which often makes it difficult for a company to dedicate itself to this technology.

In conclusion, Explainable AI can help overcome mistrust about AI adoption. The XAI can make AI decision making more transparent, increase people's trust in AI, and improve AI reliability. In addition, XAI has numerous applications in various fields. However, there are also some limitations that need to be considered. Nevertheless, XAI is an important step forward in AI adoption and could have a significant impact on our daily lives.

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