Ever heard of explainable AI (XAI) and would like to learn more about it? In this article, you will learn about explainable AI (XAI), including its benefits and limitations.
However, it is important to note that explainable AI (XAI) is not the same as XAI, the new company launched recently by Elon Musk.
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What is Explainable AI (XAI)? Introduction
Explainable AI (XAI), also known as Explainable Machine Learning (XML), or Interpretable AI, is artificial intelligence (AI) that has been designed to describe its rationale, purpose, and reasoning behind predictions or decisions to humans in such a way that anyone can easily understand.
The primary purpose of AI (XAI) is to increase the trust of humans in AI by helping them understand the logic behind machine learning algorithms and AI. It plays a critical role in the fairness, accountability, and transparency machine learning model (FATML) and is often discussed in relation with deep learning.
Understanding an AI system is very useful for businesses and individuals, especially when using it for more complex
Information Provided by Explainable AI
Explainable artificial intelligence provides data on the decision-making process of an AI by disclosing:
- Criteria used by the program to make a decision, including the data set it is based on and AI algorithm running it.
- The strengths and weaknesses of the AI model, including the levels of accuracy, bias and reliability present in its responses.
- Reasons for making a particular decision. Can the system explain why it makes certain recommendations, or the reasons behind its actions?
- Errors that the program is prone to – this highlights the model accuracy and its knowledge limits.
- Ways to correct errors. This is useful for the end-users in case they need to present the information provided by the AI tool to the public.
- The amount of trust for various decisions.
Examples of Explainable AI
Explainable AI is applicable in various industries. Below are some examples:
Financial: Explainable AI is used in the financial sector to deny or approve claims like mortgage or loan applications. It is also used in detecting fraud.
Healthcare: It helps in patient diagnosis and building trust between physicians and AI medical systems.
Autonomous vehicles: It is used in explaining driving-based decisions, especially in terms of safety. That way, passengers can feel safer knowing how the vehicle makes driving decisions and its capability.
Benefits of Explainable AI
Some of the benefits of AI (XAI) are as follows:
- Improves artificial intelligence systems
- It helps makes AI systems more trustworthy
- Gives insight on how to prevent adversarial attacks
Limitations of Explainable AI
While explainable AI has several benefits, it also has some limitations, which are as follows:
- Difficulty in training
- Lack of privacy
- Lower performance compared to black box models – although this isn’t always the case.
Final Thoughts
Explainable AI tools, also known as interpretable models, are important for maintaining hope and trust in artificial intelligence. As we move towards greater dependence on AI, this becomes ever more vital.
However, once again we have to remind you: Explainable AI (XAI) is a very different thing from the recent AI company owned by tech magnate Elon Musk. The fact the two share the same name is purely a coiendidence.
