Explainable AI (XAI) is a field of research that seeks to make AI systems more transparent and understandable. By explaining how AI systems make their decisions, XAI can help us to trust these systems and to use them more effectively.
What is the goal of the explanation? Is it to help users understand how the AI system works? To identify potential biases in the system? To comply with regulations?
The purpose of the explanation
Who is the explanation for? Technical users? Non-technical users?
The target audience
How detailed should the explanation be? Should it be a high-level overview or a detailed breakdown of the decision-making process?
The level of detail
How should the explanation be presented? Text? Visualizations?
The format of the explanation
How accurate should the explanation be? Is it important to explain every detail of the decision-making process, or is it more important to provide a general understanding?
The accuracy of the explanation
Is the explanation fair and unbiased? Does it avoid attributing decisions to protected characteristics, such as race or gender?
The fairness of the explanation
The complexity of the AI system will affect the difficulty of developing an XAI system. More complex systems will require more complex XAI methods.
The complexity of the AI system
The availability of data will also affect the development of XAI systems. If the data that is used to train the AI system is not available, then it will be difficult to develop an XAI system that is accurate.
The availability of data
The cost of developing and maintaining XAI systems will also need to be considered. XAI systems can be complex and time-consuming to develop, and they may require ongoing maintenance.
The cost of developing and maintaining XAI systems