Companies have to get better at explaining the data behind personal recommendations

Ryan Bigge makes some very good points in his post about better personalized recommendations through transparency and content design:

Data-driven companies know something that the user doesn’t. Yet the language used to convince people to act on recommendations lacks variety and explanatory power.

Algorithms aren’t neutral — or as Ryan puts it:

Every facet of machine learning is fueled by human judgement, so it must be multi-disciplinary.

Users are getting more skeptical about where these magical recommendations for what to watch, listen to, and buy come from. To establish and build trust, companies have to get better at explaining exactly why they’re recommending a specific product or action.