What we do
open positionsblog

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

Let's find out together what are the practical applications and future of this revolutionary technology.

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.

What is Artificial Intelligence and why it is useful for businesses

Let's find out together what are the practical applications and future of this revolutionary technology.
Our Insight

What is Artificial Intelligence and why it is useful for businesses

Let's find out together what are the practical applications and future of this revolutionary technology.
Our Insight

What are the main advantages of Explainable AI

AI is increasingly present in everyday life: we find it in our cell phones, in the home, and in many of the systems we use on a daily basis. However, many people are still wary of adopting AI because of a lack of understanding of the decisions it makes. At Frontiers, we believe that artificial intelligence should not replace human beings but enhance their capabilities, and Explainable AI goes exactly in this direction.
Pillar Graphic

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.

How Explainable AI works

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.
  1. Probabilistic graphical models
    Probabilistic graphical models use probability theory to determine how different variables affect AI processes.
  1. 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.
Pillar Image

Explainable AI, the fields of practical application

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.
  1. Information security.
    XAI is useful in identifying vulnerabilities in computer networks, potential hacker attacks, and improving the security of computer networks.
  1. 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.

Are there any disadvantages to using Explainable AI?

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.
  1. 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.


  • Gunning, D. (2017). Explainable artificial intelligence (XAI). Defense Advanced Research Projects Agency (DARPA).
  • Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., & Pedreschi, D. (2018). A survey of methods for explaining black box models. ACM Computing Surveys (CSUR), 51(5), 1-42.
  • Kim, B., Gil, Y., & Kim, J. (2019). Explainable artificial intelligence: an overview. IEEE/CAA Journal of Automatica Sinica, 6(4), 847-867.
  • Samek, W., Wiegand, T., & Müller, K. R. (2017). Explainable artificial intelligence: Understanding, visualizing and interpreting deep learning models. arXiv preprint arXiv:1708.08296.
  • Lipton, Z. C. (2018). The mythos of model interpretability. arXiv preprint arXiv:1606.03490.
  • Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608.
  • Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5), 206-215.
  • Holzinger, A., Langs, G., & Denk, H. (2019). Causability and explainability of artificial intelligence in medicine. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 9(4), e1312.
  • Chen, I. Y., & Dubrawski, A. (2019). Explainable artificial intelligence for medical diagnosis. Journal of Healthcare Engineering, 2019, 1-10.
  • Lamy, J. B., Séroussi, B., Griffon, N., Kerdelhué, G., Jaulent, M. C., & Bouaud, J. (2018). Toward a formalization of the process to select IMIA Yearbook best papers. Methods of Information in Medicine, 57(S 01), e1-e10.
  • Jovanovic, M., Grujic, B., & Delic, V. (2019). Interpretable machine learning in human resources. IEEE Access, 7, 161796-161809.
Subscribe to the newsletter
Iscriviti alla newsletter

Get in touch with us

Privacy policy
Copyright 2024 Frontiere
Headquarters in Via Oslavia, 6 - 00195 Rome, RM | C.F. and VAT 17464921000
linkedin facebook pinterest youtube rss twitter instagram facebook-blank rss-blank linkedin-blank pinterest youtube twitter instagram