Reposted with permission from Wrench in the Gears.
The future is uncertain and unlikely to play out exactly as described. Nevertheless, we must begin to comprehend how technological developments combined with concentrated power and extreme income inequality are leading us to increasingly automated forms of oppression. My hope is that communities will begin to incorporate an understanding of this bigger picture into resistance efforts for public education and beyond. Let us join together, embracing our humanity, to fight the forces that would bring us to “lockdown.” How can we preserve our lives and those of our loved ones outside the data stream? How can we nurture community in a world where alienation is becoming normalized? What do we owe one another? What are we willing to risk? I have divided my story into seven parts. I hope you’ll read along and consider sharing it with others.
This installment highlights smart city surveillance and the Internet of Things. Cam and Li’s lives, including their educational experiences, are shaped by ubiquitous algorithms that align their behaviors to the economic and social expectations put in place by the Solutionists. This is the third installment in the series. If you want to read from the beginning use this link to access the introduction and Part 1: Plugging In. The whole series can be accessed here: Link
Cam and Li have grown up in a world controlled by sensors and data. All day, every day sensors watch, track and transmit information. The devices that make up the vast web of Internet of Things are tiny, but their combined power is incalculable. The most common IoT sensor in the pre-lockdown years was the smart phone. Practically anyone over the age of ten had one. Acting as a sensor, people’s phones were a primary means of data collection, logging information about how people interacted with each other, with systems, and their physical world.
The first sensors were created to monitor global supply chain shipments. Then, corporate, government and academic researchers devised a dizzying array of sensors to transmit data about most aspects of the physical world and how people live their lives in it. Instead of tracking pallets on cargo ships, they now track people, buses, energy, animals, art, storm water runoff, even sounds and footsteps. Each processor gathers a particular type of information that can be merged into the data stream for analysis. Predictive analytics algorithms, complex mathematical equations that anticipate future outcomes, tap into the data stream. Such algorithms can be used to predict when the bulb in a streetlight will fail, when a storm sewer will overflow, or even where a crime will happen.
For years authorities quietly built datasets that digitally documented community life using police body cameras and later cameras embedded into robot patrols. It showed incredible hubris to roll out such a program under the guise of citizen protection. The cameras, of course, were always looking out at the people, not at the police. Even with footage, police were rarely held accountable for crimes committed. Meanwhile, all aspects of people’s daily lives were taken in; faces, routines, social connections; anything within the field of view of the camera was absorbed by Oracle.
That such data would be turned against citizens in times of civil unrest should have been anticipated. Some who lived in communities that had experienced the evolution of brutal policing were indeed skeptical, but many held on to the idea that the cameras were well intentioned. Cam’s mother vividly remembers the week of the lockdown, how teams were deployed strategically throughout the city in ways that made resistance futile. All those years, the police state’s neural networks had been “learning” their neighborhoods and their faces all in the name of public safety.
Post lockdown, sensors and technology have been integrated into more and more aspects of daily life, pressuring people to make “good decisions.” Strivers feel less and less in control of their daily activities. They await the next haptic pulse that will direct their attention and actions. Cam might crave a pint of chocolate ice cream, but her minder is watching the refrigerator and uses guilt to pressure her into choosing carrots and celery instead. If she doesn’t comply, it will most certainly go into her health data log. Maybe Li wants to sleep late. Well, the sleep monitor strives to keep her on a productive R.E.M. cycle, so it raises the shades in her bedroom and turns on the shower down the hall at the appropriate hour. Is Talia driving to the corner store when she should be walking? Well, her auto tracker knows, as does her step counter, which means her insurance providers know, too. Maybe she can get away with it early in the month, if she has time to make up her activity quota before the 31st. Resources for healthcare are so constrained that people must demonstrate through data that their personal routines and lifestyle choices optimize preventative health protocols.
The Nudge Unit is constantly looking for new ways to incorporate behavioral triggers and feedback loops into online education and VR platforms, too. Buzz, buzz, a text appears. “Cam needs more points on Skyward Skills. It’s time to log on.” Or the pulse monitor indicates Li is too tense. Buzz, buzz, “Take a mindfulness break kid,” breathe and reflect. Buzz, buzz, “Talia step away from the screen and walk around the block to avoid blood clots.” Action triggered, data logged, repeat has turned life into one unending Pavlovian experiment.
Existence has subtly shifted to align to the Solutionist outlook. Economic forecasters rely on people being rational actors as they develop financial projections, and if technology can be used as a tool to shape human behaviors and enforce “rationality,” it is all the better for the global financiers who generate their wealth by speculating on the lives of everyday people. For the strivers, optimization has erased freedom and personal agency.
In the post-labor era, people have become more valuable for the data they produce than for their capacity to do physical work. Thus all but the off-liners have been integrated into the global corporate value chain as commodities. With biometrically-enabled Citi Badges, Cam and Li are not unlike tagged calves or farmed salmon, managed and processed without agency or recourse; lives controlled for the profit of others. The bio capitalist economic model values them only to the extent that they contribute their digital labor to the Solutionists’ data-driven system of outcomes-based results.
Algorithms hold tremendous power over Cam and Li. Using data generated through the Internet of Things, Oracle can make predictions about the type of adults the children are likely to become. What their cost to society will be. What they might contribute as human capital. Should their family should fall into poverty, Oracle can evaluate how much profit there could be made providing services to “impact” their situation through Pay for Success contracts. Would the predicted rate of return on their lives justify expending the Global Coin required? The Solutionists say, “Just run the data; the data will tell us.”
Talia tries to shelter the family from the data stream as much as possible, but that is has proven difficult. Accessing any public services demands data. Walking outside means you are under surveillance. Even at home devices keep tabs. Data has also become a currency people use to supplement their insufficient Global Coin stipends. The pretense that a person “owns” their own data and can monetize it is supposed to make them feel better about their situation. It doesn’t. Each data transaction puts another piece of one’s soul on the auction block, scrutinized by a predatory system that thrives on want and suffering. And it’s always a buyer’s market. No person in need is going to get ahead selling bits of data. These transactions are just stopgaps until the next Citi Badge stipend hits, a release valve that has thus far kept rebellion at bay.
At first the sensors seemed innocuous, uploading information about when a trashcan was full or telling people where parking spots were available. There were sensors that monitored air quality and ones that made sure streetlights were efficiently managed. People were enthusiastic. But then came the noise sniffers, and the motion sensors, and the drones. Parks and recreation officials were brought on board and encouraged to incorporate cyborg roses into public landscape projects. When first introduced, people were astonished at Eleni Stavrinidou’s work transforming plants into transistors, and now there were rumors of computational forests being grown in remote outposts. Once plants had sensors, people started to get really worried.
Teachers never imagined how sensors would alter classrooms and eventually eliminate them altogether. Adoption of 1:1 devices eroded teacher autonomy until students were spending most of their day with volunteer aides, eyes glued to screens. The teachers that remained were left evaluating student data. In classes where teachers were still allowed to lecture, movement, vibrations and sounds were monitored through sensors embedded in seats. The aim? Supposedly to provide continual feedback regarding student engagement and quality of instruction, but everyone knew it was really to keep track of the content delivered and how students responded. It was chilling.
By that point, the last remaining veteran teachers abandoned the profession. Eventually teacher shortages, austerity budgets, and the corporate education lobby’s campaign for “anytime, anywhere” learning ushered in IoT-enabled learning ecosystems. No one had invested in public education infrastructure for years. Sending everyone home with a device meant there was no longer the expense of feeding poor children. Students too young to stay at home and whose parents were working strivers were packed off to community partners. These partners had been carefully prepared for their role providing standards-aligned summer and out of school time programs. Plus this approach brought education completely under the umbrella of social impact investing, which pleased the financiers. All in all it was a pretty seamless transition. Given how punitive the instruction had become, most felt a sense of relief when the time came to phase out schools entirely.
Ten years out Cam and Li, like the characters in Isaac Asimov’s short story The Fun They Had, have no idea what “going to school” means. Some nights before turning out the lights, Talia tells the girls stories that give them a glimpse into that past. Yet, it is so far removed from their reality that neither can imagine what it must have been like to learn with a group of other kids. To have a human teacher and books, and go to a school building and spend the day there is a frightening prospect. People live isolated lives. Encounters with others are carefully managed. To spend a full day as part of a group, talking no less, seems a perilous and fraught enterprise.
Now everyone is assigned an Artificial Intelligence (AI) “assistant,” a lifelong learning guide when they receive their first education voucher. Cam tolerates hers, but Li is another story. They have quite the adversarial relationship. Li accuses her AI of giving her assessments that underestimate her actual ability, so she has to spend days and days going over material she already knows. Her games are always shorting out at a critical moment, right before her points are logged. The algorithm gives her essays failing marks, even though her mom and Grandpa Rex both say she has a gift for creative writing. Cam says that because the companies are rolling out so many new programs, glitches just going to happen and to not take it personally. People have always had frustrations with their devices, from autocorrect fails to systems freezing unexpectedly, but now that devices control so much more of people’s lives their faults are harder to tolerate. Talia often finds herself having to get up from her work and do a hard shutdown of Li’s tablet to give them both a time out.
The AI conversational agents and the platforms that host them employ a variety of tactics to ensure that Cam, Li, and all the children remain on task. Devices record ISPs and timestamps for logins. Keystroke and facial recognition data is stored, too. Wearable and biometrics are part of the equation. The early headbands and wristbands were incredibly clunky, but five years in they switched to IoT temporary tattoos with sleek designs that prominently identify each child’s designated pathway and rank.
It’s a major milestone when a student attains enough credentials in their portfolio to upload and claim a pathway. The tattoos, not unlike military insignia, help communicate social order and expected etiquette when new people meet. A picture is worth a thousand words, and in a culture that is increasingly non-verbal, a pathway tattoo is an important tool.
To maintain order, the Solutionists knew behavioral engineering had to become central to the educational system. With little meaningful work, systematic mental health training was needed. They wanted people neither too depressed nor too rebellious. Resilience, and grit were traits instilled through apps and gamification; children’s mindsets tracked as closely as the knowledge they acquired. The system was calibrated to identify mental disorders and dissidents early, flagging them for intervention. Both Cam and Li knew kids who had been forcibly plugged into remediation, but it wasn’t discussed openly.
The isolation that resulted from cyber education took a toll on many. Social networks withered. Kids rarely spent time with friends face-to-face. Text-support only went so far in beating back the darkness. Suicide rates climbed, affecting younger and young children. Programmers scrambled to develop new monitoring procedures. The Global Well Being Program was a leader in the field, their cutting-edge algorithms effective, but expensive.
Despite the high cost, sector education officials from all but the poorest communities debited funds for the monitoring service directly from student vouchers to cover the cost. Timely intervention was a matter of life or death, and people were willing to pay. In the post-labor world, monitoring and treating depression was a growth market. Before long tele-therapy and mental-health VR surged past bio-pharmaceuticals as darlings of the venture capital investment crowd.
By 2025 most major and mid-size cities had become “smart cities,” integrating IoT sensors into a wide variety of infrastructure projects. In doing so, officials created a ubiquitous layer of surveillance across the public sphere. Now, in order to access communal spaces, residents had to acquiesce to being watched. Management of the complex IoT systems required expertise far beyond the in-house capacity of most cities; as a result, outsourcing to global corporations became commonplace.
Over time, voters found they had less and less voice in government. Officials kept up appearances for several election cycles, but it became obvious that technology companies like Sysko were really the ones in charge. People wanted to believe elections still mattered. The history modules made a point of expressing how hard people had fought for the right to vote and to fix problems like gerrymandering, but it the years leading up to lockdown it became a hollow exercise. Talia had memories as a teen of the media stirring up outrage over voting irregularities. Looking back, they should have realized something was amiss. The solution to this “problem” was to switch to voting on the Blockchain using Citi Badges. Of course that shift effectively shut all of the off-liners, those who had no badge, out of the process.
Democracy was exposed for the charade it had always been, and it became clear to all that they had been living under fascism for a very long time. The cloud-based computing, telecommunication, and global finance interests united under the Solutionist banner and ensured authoritarian control was firmly in place. Global law enforcement working through the Blockchain Collaborative backed the technocrats in their coup. Now for Cam and Li, voting was a topic touched upon briefly in history modules where it was framed as a messy process no longer suited to the well-structured, transparent society the Solutionists had devised.
As the end game neared, secure and exclusive sanctuaries modeled after billionaire and media mogul Richard Braddock’s island home began to appear. He was among the first to bring world thought leaders together to discuss ways to build and scale Blockchain applications. These thought leaders sold everyone a utopian vision of trust, transparency and collective support. Those purported values fell by the wayside, though, shortly after the lockdown.
People with knowledge of edge computing, IoT, and Blockchain deployment and who had the money constructed sensor free zones to which they could retreat. Of course kids like Cam and Li will never be able to obtain access to such sanctuaries. That world is limited to families that can afford the astronomical costs of having human teachers for their children, whose social networks are such that they don’t need citizen scores or e-portfolios to assert their value to society. Sometimes Cam and Li wonder about the sanctuary kids. Surely there aren’t many of them. Are they lonely? Do they feel isolated, too? Are they glad to be unplugged? Do they know about life on the outside, life on the ledger?
Continue to Part 4: Data Mining Life on the Ledger
Internet of Things IBM: Link
History of IoT Sensors: Link
What is Blockchain: Link
Supply Chain IoT: Link
Cash VS Digital Economy and Online Payments: Link
Sidewalk Labs: Link
Smart Cities / Noise Sniffer: Link
IoT and Predictive Policing: Link
Police Body Cameras and AI: Link and Link
Patrol Robots: Link
Street Lights and IoT: Link
IoT Parking: Link
Storm water IoT: Link
Smart Trash Cans: Link
Sensors and Smart Cities: Link
Cognitive Drones: Link
Cyborg Roses: Link
Internet of Battlefield Things: Link
Pay for Success and Big Data: Link
Blockchain Social Impact Token: Link
Human Capital Analytics: Link
Nudge Unit: Link and Link
Game Theory, Human Resources and Social Skills: Link
AI Nudge Bots: Link
Behavior Change for Good: Link
Haptic Devices: Link
Rational Choice and Behavioral Economics: Link
Education and Biocapitalism: Link
Behavioral Science and Social Impact: Link
Making Behavior Change Stick: Link
IoT Classrooms: Link
Sensors Determining Education Quality: Link
Affectiva Emotion Sensing Software: Link
Behavioral Biometrics: Link
World Well Being Project: Link
The Fun They Had: Link
Device Use Behavior Tracking in Education: Link
Virtual Agents / USC Institute of Creative Technologies: Link
AI Conversational Agents / Amelia IP Soft: Link and Link
AI Teaching Assistant: Link
Conversational Agents / Articulab: Link
Applied Gaming and Mental Health: Link
Brainwave Data Collection: Link
IoT Tattoos / Duoskin: Link
Pathways to Prosperity / Jobs for the Future: Link
Characterlab / Grit: Link
CASEL / Social Emotional Learning: Link
Serious Games and Mental Health: Link
Government as Platform: Link and Link
IBM Smart Cities: Link
Cisco Smart Cities: Link
New York Smart City: Link
Blockchain Voting: Link
Neckar Island Blockchain Summit: Link
Edge Computing: Link
Blockchain Cryptoeconomics: Link
Blockchain Alliance: Link