Black-Box AI

Explainability is extremely crucial in systems that are responsible for carrying out mission-critical tasks. For example, in healthcare, if scientists are relying on AI models to help them figure out whether the patient will have cancer or not, they need to be 100% sure of their diagnoses otherwise this can result in death, a lot of lawsuits and a lot of damage done in trust. Nature is this problem is so intense that explainability sits at the core of this problem: the data scientists and the human operators, in this case, the doctors need to understand how the machine learning system is behaving and how did it come to a decision.

Explainable AI is also important in finance or fintech in particular due to the growing adoption of machine learning solutions for credit scoring, loan approval, insurance, investment decisions and so on. Here again, there is a cost associated with the wrong decisions by the machine learning system: so there is a huge need to understand how the model actually works.

Using black-box AI increases business risk and exposes the businesses to a deep downside — from credit card applications to determining disease to criminal justice.

The reason why black-box models are not desirable becomes more clear when we look at how the business functions as a whole:

For the business decision-maker, data scientists need to answer the question of why they can trust our models, for IT & Operation, data scientists need to tell them how can they monitor and debug if an error occurs, for the data scientist, they need to know how they can further improve the accuracy of their models and finally, for regulators and auditors, they need to be able to get an answer to whether our AI system is fair or not?

Enter explainable AI

Explainable AI aims at providing clear and transparent predictions. An end-to-end system that provides decisions and explanations to the user and ultimately provides automated feedback to constantly improve the AI system. Remember, xAI is highly driven by the feedback so is a two-way interaction between the human and the AI system.

In the end, we should understand why behind the model, the impact of the model, where the model fails and what recommendations to provide.

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