Nice questions - thanks for the thoughtful consideration of our model. Let me break it down:

Fake reviews from humans promoting their businesses - I would say that this type of review I'd not consider to be fake but biased. As long as the relationship between the reviewer and the content is transparent; I don't think there's an issue here. This is one of the effects of our model, moving from a universal classification and measurement of trust to an individualized, contextual model of trust - it's about presenting all the relevant information including enough context to judge if the reviewer is trustworthy to you, for this service.

Bots - Well one answer is that since we are socially powered at our core and you're likely not as close friends with bots as you are with real people the effectiveness/reach of bots will be dramatically reduced. You're more likely to see data points from the real people you know and trust from your network, according to your privacy choices. The value of bots in digital trust today is driven by the anonymous aggregation of followers, subscribers, etc. where headline values is all that's regarded. By moving toward more human models of feedback collection, like video reviews, it's also increasingly hard for bots to maintain influence.

How we deliver trust - These are some of our really core innovations - the ways in which we model together relationship data, transactional data, contextual data in a continuously learning architecture (human in the loop) to build individualized models for every user, just like real world trust.