Explainability
When algorithms are used for critical cases, it becomes important to have the ability to explain the reason behind each and every result whenever needed. This is necessary to make sure that decisions based on the results of the algorithms do not introduce bias.
The ability to exactly identify the features that are used directly or indirectly to come up with a particular decision is called the explainability of an algorithm. Algorithms, when used for critical use cases, need to be evaluated for bias and prejudice. The ethical analysis of algorithms has become a standard part of the validation process for those algorithms that can affect decision-making that relates to the life of people.
For algorithms that deal with deep learning, ...
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