Explainable AI methods explain how Artificial Intelligence-powered decisions are reached. Think about it, you’re more likely to trust and consent to your employee if they give you a reason (I’m not coming into work today vs. I’m not coming into work today because I had to rush to the hospital).
So, you want to explain to your customers how a specific algorithm output was provided: Why they got that recommendation, or what they can do to improve their chances of getting their loan approved? But they do not understand your eXplainable Artificial Intelligence results. In this article, I show you how to design explanations so that users understand them better.
The team at Google has mastered the art of providing users just enough information to encourage them to use their digital products. In this post, I analyze how they have used explanations to make it clear that the customer will benefit by giving consent to their privacy policy.
