Explainable AI (XAI)
What is Explainable AI?
Explainable AI, or XAI, is a collection of procedures and tools used to help and support machine learning algorithms and the overall ML modelling. It aims to produce a trustworthy understanding of the models’ predictions and outputs. With the rapid advancement of the artificial intelligence field, the processes and operations behind machine learning modelling becomes more complex and harder to be comprehended. Unable to understand a ML model refers to a “black box”.
The use of XAI helps organisations, businesses, and researchers recognise the expected impact as well as potential issues from a machine leering model. it also helps describe the model, by looking at its accuracy, transparency, , behaviour, fairness and bias, and improvement.
There are several benefits of implementing XAI. The use of such methods can support model monitoring, analysis, and further developmental improvement. Explainable AI can assure developers and companies whether the system works properly and efficiently. Organisations within the industry may also have to follow regulations and standards.
Next: Ethical AI
