Well they are just an average of all the individual ratings, no? Well that approach would be very manipulable, and you see right away that they place more weight to people that have bought the product. Turns out it's much more than that. It's machine learning and lots more that is proprietary and un-revealed. So not transparent. And in a complex context. Which makes you wonder what transparency for this example of machine learning means. Even if we were given the exact set of algorithms used, what use would they be? Depends also on the data used to create the learn the algorithms. And how we as customers plan to use the ratings. So can we really be transparent in AI? Any more that a human can be transparent? Quote below made me think this, good article:
What do Amazon's Star Ratings Really Mean? In Wired By Louise Matsakis
" ..... Starting in 2015, Amazon began weighting stars using a proprietary machine-learning model. Some reviews now count more than others in the total average, based on factors like how recent they are and whether they come from “verified” purchasers (meaning Amazon could confirm the reviewer actually bought the item they claimed to love or hate). David Bryant, an Amazon seller who also blogs about the company, believes Amazon may also take into consideration factors like the age of the reviewer’s account and the average star rating they usually leave. “There appears to be some discount applied to reviewers who predominantly leave negative reviews,” he says. .... "
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