Many ML algorithms are black boxes — you input a lot of data, and get a model that works in mysterious ways, which makes the results difficult-to-impossible to explain.

the bar is for your user to see blatant errors once in a while (which they likely will) and still trust your results overall. That’s a high bar to meet.

it is important to think in advance about the data and components of the model that the user may want to see and how to present results in a way that builds user trust.

When deciding how to show results, the goals should be to make them clear, believable and most importantly — actionable.

Backdating. This is taking historical data and plugging it into the model to produce past predictions that could be verified against known values.

Simply telling the user what the types of data you took into account in your model builds trust by helping them see that the decisions are based on the same types of variables they would consider themselves if they had to make this decision

Simplifying and only showing select results to facilitate decision making.

Defining a new metric. One question you should consider is whether you’re creating a new metric (a new type of “score”) or predicting a well understood one

It may be a good idea to consider giving results in ranges, deciles, grades or some other less precise measure of value rather than showing the customer the exact output of your algorithm.

The key takeaway is to not underestimate the amount of thought and effort you need to put into the user experience side of the problem — even the best model is useless if users can’t understand, trust or act upon its output.