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Explainable AI; XAI

1. What is Explainable AI?

As systems based on artificial intelligence have become more complex in the recent years, it is becoming increasingly difficult for humans to intervene in model construction and decision making processes. Artificial intelligence is sometimes called a 'black box' in the sense that it is difficult to fully understand the process because it makes decisions after complex calculations using deep neural networks. In this situation, the need to understand how artificial intelligence made such decisions became necessary, and the concept of explainable artificial intelligence emerged. In other words, explainable artificial intelligence is a feature of artificial intelligence that aids users to understand artificial intelligence's complex decision making processes. 

2. Terminologies

Below is an explanation of various terms that appear in various papers and books. 
✓ Understandability
This refers to the ability of a model to describe the results so that humans can understand it without having to describe the complex computational processes or algorithms in it in detail.
  ✓  Comprehensibility
It is defined as the ability of an algorithm to express learned knowledge in a form that humans can understand.
  ✓  Interpretability
Interpretability is defined as the ability to interpret meaning in terms that humans can understand.
  ✓  Explainability
Explainability is the concept of the interface between humans and decision makers, defined as the ability to explain meaning.
  ✓  Transparency
Transparency refers to a characteristic that can be understood by the model itself, and if the model is understood by itself, the model is considered to be transparent.

3. Explainable AI (XAI) with CLICK AI


The purpose of AI is to help humans make better decisions effectively, so businesses realize the true value of AI solutions when users make decisions based on AI outputs and predictions. CLICK AI could provide support the decision making process of businesses by analyzing and visualizing variables that affect the outcome, and automatically providing visualizations to gain key business insights.

Reference
http://ceur-ws.org/Vol-2071/CExAIIA_2017_paper_2.pdf
https://www.sciencedirect.com/science/article/pii/S1566253519308103

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