I’m starting to think that managing expectations are the most important aspect of a great digital product experience. I was reading through OneCart’s reviews and saw one that reads as follows: This reminds me of a poster I once read in my Sales class “formula for success: under-promise, over-deliver.” – Unknown In UX, principles are …
Using explanations to create human-centered technology has the ability to improve your customer satisfaction levels. But, technology that appears human has the ability to cause customer disappointment.
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.
Although XAI can help the user understand and trust outcomes, it can also lead to confusion if not used effectively.
