Artificial Intelligence helps develop automated decision systems configuring service features including tailored pricing (incorporating risk assessment) for each customer. Customers request explanations on the automated decision. Such explanations can be logical steps summarizing the machine decision process.
Where is it easiest to start? Use cases where explainability is within reach?
Insurance policy for a car, a house (risk coverage)?
Investment plan matching customer needs and Financials, as a banking product?
Health decision like surgery/no surgery?
I feel " Investment plan matching customer needs and Financials, as a banking product" will be the easiest and low risk use case.
In AI humans plays the role to trigger domain data via a machine and perform some actions according to the training on domain's data/information. I other words we transform some information with imposed limitations on domains data for decisions by providing new unseen assurances, events, objects, etc. This way of providing commands to some mapping for providing decisions in the presence of gained training/experiences is called AI. The resolver which accepts mathematical functions (natural arguments based on truths) or functional for input to output representation is called machine. Machine perform decisions/works via mathematical/statistical formulations ( input to output operators) are called models.
@Muhammad Ali, thanks for your input on AI. Can you give your view on low hanging fruits = more easily attainable use cases
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