I know I personally gave plenty of three star reviews on Lyft before I learned better, meaning them as "no complaints". Who knows what happened to those drivers as a result. Why even invite the misunderstanding?
In France for instance, it's very rare for a student to go above 15/20 or so at the college level (I graduated in the top 10% of my class with a 12/20 average; the top ranked student, whom I was friends with, had a 14/20 average).
But when I graded homework as a graduate student for an american university, the professor told me I was too harsh, and after adjusting my criteria to match what she wanted, I found myself giving many 100/100.
When it comes to ratings used by others to evaluate, no system is more customer and vendor-hostile than one where everything that meets a minimum threshold for adequacy should be rated as perfect.
I guess, at least a small step would be to make it so values less than the highest rating don't count against you so negatively - it should be ok to be a 3/5 or 7/10.
It isn't just online, and it isn't just a technology problem. I recently bought a car and the manufacturer sent me a survey about the process. The salesman made sure, numerous times, to let me know that I'd be getting said survey and that anything less than a 10 meant he failed and that there would be negative consequences for him. Since I was mostly happy with the process, I gave him all 10s, even though one or two areas I would have rated a little lower. He got his good review and it didn't really bother me, but the car company lost out on some potentially useful feedback.
Some of the problems - like different people having a tendency to rate on different scales, ratings coming from different factors, etc - seem pretty amenable to a ML approach to make reviews a lot more useful, though. I wonder if anyone outside of a handful of companies (Amazon, Yelp, AirBnB, Google, etc) has enough data to make it useful, though?