User Experience with Machine Learning – Towards Data Science – Medium

Machine learning is known for its difficulties with interpretability, or rather its absence. Which is an issue if your users have to work with the numeric output, like in the systems used in sales, trading or marketing. If the user's interpretation of the ML output is wrong the actual metrics won't matter and you end up with the bad user experience. The problem is even bigger if you try switching users from an old transparent algorithm to ML -- dissatisfied users may try pushing back against switching to ML. Also, as counter-intuitive as it sounds, the mathematical competence of the users might play against you as the most experienced users will give the hardest push-back.