Google thinks I’m interested in parenting, superhero movies, and shooter games. The data broker Acxiom thinks I like driving trucks. My data doppelgänger is made up of my browsing history, my status updates, my GPS locations, my responses to marketing mail, my credit card transactions, and my public records. Still, it constantly gets me wrong, often to hilarious effect. I take some comfort that the system doesn’t know me too well, yet it is unnerving when something is misdirected at me. Why do I take it so personally when personalization gets it wrong?

Right now we don’t have many tools for understanding the causal relationship between our data and how third parties use it. When we try to figure out why creepy ads follow us around the Internet, or why certain friends show up in our newsfeeds more than others, it’s difficult to discern coarse algorithms from hyper-targeted machine learning that may be generating the information we see. We don’t often get to ask our machines, "What makes you think that about me?"

Personalization appeals to a Western, egocentric belief in individualism. Yet it is based on the generalizing statistical distributions and normalized curves methods used to classify and categorize large populations. Personalization purports to be uniquely meaningful, yet it alienates us in its mass application. Data tracking and personalized advertising is often described as “creepy.” Personalized ads and experiences are supposed to reflect individuals, so when these systems miss their mark, they can interfere with a person’s sense of self. It’s hard to tell whether the algorithm doesn’t know us at all, or if it actually knows us better than we know ourselves. And it's disconcerting to think that there might be a glimmer of truth in what otherwise seems unfamiliar. This goes beyond creepy, and even beyond the sense of being watched.

We’ve wandered into the uncanny valley.

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Since the 1970s, theorists have used the term "uncanny valley" to describe the unsettling feeling some technology gives us. Japanese roboticist Masahiro Mori first suggested that we are willing to tolerate robots mimicking human behaviors and physical characteristics only up to a point: When a robot looks human but still clearly isn’t.

The threshold is where we shift from judging a robot as a robot and instead hold it against human standards. Researchers at the University of Bolton in the UK have described this shift as the "Uncanny Wall" in the field of digital animation where increasing realism and technological advancements alter our expectations of how life-like technologies should be. I would argue that we hit that wall when we can't distinguish whether something is broadly or very personally targeted to us. The promise of Big Data has built up our expectations for precise messaging, yet much of advertising is nowhere near refined. So we don't know how to judge what we are seeing because we don't know what standard to hold it against.