AI Products need to be transparent and explainable.

AI products must have trust at their core and one of the pillars of trust is transparency and explanation. High trust AI products have lower churn and higher retention so evaluating the trust of your product is super important. There are a number of strategies you can implement which I will talk through in this newsletter to help your users trust your product. To help communicate and demonstrate some of the strategies in this newsletter I've designed and built a demo project called Onit which is a mobile personal assistant helping you manage your personal and work life in one place.

The three strategies I'll cover build on each other: how sure the AI is, how it reached its conclusion, and what information it drew on to get there. Let’s get into it. A common problem with AI models in particular LLMs is they will produce incorrect results but present them as facts in a confident manner which has obvious negative consequences for your users such as them taking the wrong actions, believing misinformation and in mission-critical systems can lead to extremely dangerous situations including loss of life. This leads me to the first strategy to make your AI product and system transparent: Confidence Signalling, which helps you surface AI uncertainty visibly and consistently. A 60% confident output should never be presented identically to a 99% confident one. You can implement this in various ways using language, visual weight, or explicit probability scores to communicate this distinction.