Do you know what they know about you?
Cities and companies are increasingly deploying networks, sensors, and data systems with hopes of greater optimization, security, and economic growth. From ubiquitous CCTV networks, instrumented utilities, and police body cameras to identity-based transit cards, public & private wifi, and a rich tapestry of location-based services, the urban landscape is being woven with pervasive measurement, recognition, and modeling.
The platforms are surrounding us with their eyes and ears…
The same pattern is unfolding across well-heeled businesses that are instrumenting their supply chains, logistics, facilities, and operational teams, bolting on machine learning to drive efficiencies and competitive advantage.
Uber has an extraordinarily rich data model of urban mobility. They likely know more about municipal transit than cities themselves. Facebook consumes location data, photos, videos, mentions — over 50,000 unique attributes including many bought from real-world third-parties, such as income, which restaurants you go to, and how many credit cards you have. We give them billions of data points every day that they use to feed some of the world’s best machine learning systems to model us and target us with content and ads.
LinkedIn has a high-resolution view of talent and hiring trends across large industries and demographics. Google owns our search history and the contents of our email and, like Apple, recognizes the faces in our photos, the traces of our steps. Apple and others are now hiring for HR roles that bring detailed analytics and predictive modeling to find the best potential employees.
Can we really be modeled?
The platforms are surrounding us with their eyes and ears, the walled gardens are global, moving into the physical world as we become more digital. Such pervasive measurement is now slowly making its way into wearables, our clothing, the stuff we eat and drink with, our beds and pillows, our cars and satellites and… well, everything.
Distilling us complex and fickle humans down into computational models has all sorts of implications, least of which is the question of can we really be modeled? Apparently with enough data, we can.
We often share our lives and pursue our own data-optimized performance with little thought to the clouds and corporations and unknown third-parties getting their fingers into the story of our lives. If data is the new defensible barrier for business, what moat can we build around ourselves?
It’s starting to feel a bit like that apocryphal Native American belief that cameras steal your soul…
This is just the beginning of exponential information asymmetry, of digital and physical systems that know us better than we know ourselves. Ad Tech is driving a growing business in modeling each of us in incredible detail — a million billboards for a million individuals. Google and Facebook are very wealthy and very leveraged by digital advertising. We think of “social media” kind of like a big party (for good and ill) but those are two-way mirrors in the living room hiding analysts evaluating our every nod and murmur, imploring us to dance like we’re not the product.
We kinda know we’re the product but it’s starting to feel a bit like that apocryphal Native American belief that cameras steal your soul. We’re being recorded everywhere, helping artificial intelligences make copies of us, machines of loving grace modeling the human soul.
Soon, we’ll find ourselves continuously measured, identified, evaluated, and even predicted.
We routinely know far less about our digital lords than they know about us. Witness efforts like Cambridge Analytica that use social modeling as a way to identify voters on the fence and tip them towards a specific candidate with aggressive targeted propaganda. Attention economies are addressable not merely by the platform owners. Small groups can now access volumes of data and spin up sophisticated machine learning systems that greatly empower their efforts.
The exponential infrastructure of monitoring and surveillance follows urban planning, industrial efficiency, ecological governance, social networks, and pretty much everything connected. It’s now being colonized by machine vision, natural language processing, and predictive modeling. The boundaries between our digital worlds and the physical one are far more porous and blurry than we often realize. Soon, we’ll find ourselves continuously measured, identified, evaluated, and even predicted.
We all need greater data literacy.
We need more discussion about data ownership and identity, especially here in Silicon Valley where we quickly celebrate digital business models with little worry for the losers, the product, the digital serfs tending the data farms. We all need greater data literacy to become more selective about what we share digitally, to advocate for our own privacy before we’re always sharing everything wether we want to or not.