I wanted to use Mom & Pop’s Tops because it is small and based on individual transactions to drive its profit. That felt more manageable than what we’re about to encounter.
In my second post, I wrote of algorithmically-driven business and product models—specifically, ones that use the actions of users/customers to determine what those users’/customers’ experiences should be going forward. This is algorithmic inference.
John Cheney-Lippold, in his essay, “A New Algorithmic Identity”, outlines the complexity involved in how this inference occurs — more than just: “I bought my friend a package of cloth diapers for her baby shower and now Amazon keeps recommending diaper rash cream…that’s really annoying.” These are, as Cheney-Lippold puts it, “large surveillance networks online.” That is, through the use of advertisements and other tracking mechanisms, companies like Amazon get a much larger picture of not only your shopping and browsing habit, but can begin to construct your identity based on the activities of those who visit (or even, might visit) similar sites.
So now, you’re not only someone who might buy tubes of rash cream. You’re a complex amalgam of the properties associated with others who have bought those diapers and those who visited the blogs you used to research which diapers to buy. But — and here’s the part that starts to break my brain — you’re also associated with the people who have visited the same sites as the people who have also visited that blog! Let’s break that down.