The Glass Box!
Explainability in AI.
AI glass box is a system whose algorithms, training data and model are all available for anyone to see.
Researchers still sometimes characterize aspects of these as black boxes.
A black box is what contains the internal workings that are invisible to the user.
The field of explainable AI is working to develop algorithms that, while not necessarily glass box, can be better understood by humans.
Explainable AI is used to describe an AI model, its expected impact and potential biases in a set of processes and methods that allows human users to comprehend and trust the results and output created by machine learning algorithms.
Explainability means enabling people affected by the outcome of an AI system to understand how it was arrived at.
Explainability also means it’s possible to understand why a mistake was made and even train the system to stop it from happening again.
The 4 Principles of Explainable AI:
Explanation: Systems deliver accompanying evidence or reasons for all outputs.
Meaningfulness: Systems provide explanations that are understandable to individual…
