Explainable Machine Learning


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We are evolving, faster than expected, from a time when humans are coding algorithms and carry the responsibility of the resulting software quality and correctness, to a time when sophisticated algorithms automatically learn to solve a task by observing many examples of the expected input/output behavior. Most of the times the internal reasoning of these algorithms is obscure even to their developers. For this reason, the last decade has witnessed the rise of a black box society. Black box AI systems for automated decision making, often based on machine learning over big data, map a user's features into a class predicting the behavioral traits of individuals, such as credit risk, health status, etc., without exposing the reasons why. This is troublesome not only for lack of transparency but also for possible biases inherited by the algorithms from human prejudices and collection artifacts hidden in the training data, which may lead to unfair or wrong decisions. It is therefore urgent to develop a set of techniques which allows the user to understand why an algorithm made a decision.

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