Ayla Kangur, Machine Learning Engineer and Head of Responsible AI at Slimmer AI, published an article in Towards Data Science called “Explainable AI in Practice”. In the article, she talks about how you can use explainable AI (XAI) to improve transparency and improve outcomes.

XAI helps human beings understand why the machine reached a particular decision. If humans need to act on decisions made by the system, it’s often very important for these outcomes to be explainable.

In her article, Ayla shares real examples of XAI from 3 case studies — highlighting 3 different approaches. She also shares 6 steps you can take to ensure transparency when approaching a new AI project.

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