Media & Information Literacy Grade 8: Surveillance Capitalism Your Data as a Product

Grade 8 Media & Information Literacy Study Guide Topic: Surveillance Capitalism: Your Data as a Product

1. The Driving Question

"If you’re not paying for the product, you are the product"—but how does that actually work? Why do free apps, games, and websites care so much about what you click, where you go, and what you like—and how do they turn that into money without you ever seeing a bill? And if it’s your data, why don’t you get a say in how it’s used?"

2. The Core Idea — Built, Not Listed

Imagine you’re at a mall with your friends, and every store you walk past scans your face, tracks which clothes you look at (and for how long), and notes who you’re with. Then, they sell that information to a company that predicts what ads to show you later—even if you never bought anything. That’s surveillance capitalism in action, but instead of a mall, it’s the internet, and instead of stores, it’s apps, games, and websites.

Here’s how it works: Companies like social media platforms, search engines, and free mobile games collect your digital behavior—what you search, like, share, or even how long you hesitate before clicking. They use algorithms (like invisible robots) to turn that data into predictions about what you’ll do next—what you’ll buy, how you’ll vote, or even how you’ll feel. Then, they sell those predictions to advertisers, political campaigns, or other companies who want to influence you. The creepier part? You agreed to this when you clicked "Accept All Cookies" or "I Agree" to the terms of service—even if you didn’t read them.

Key Vocabulary: - Surveillance Capitalism Definition: An economic system where companies make money by collecting, analyzing, and selling people’s personal data and online behavior. Example: A free fitness app tracks your runs, sleep patterns, and food logs, then sells that data to health insurance companies to predict your future medical costs. Note: In college, you’ll study how this intersects with privacy laws, labor rights (e.g., gig workers’ data being sold), and even authoritarian governments using similar tactics to monitor citizens.

• Data Exhaust Definition: The trail of digital "crumbs" you leave behind—like search history, location data, or even how fast you scroll through a post—that companies collect and monetize. Example: If you search for "best skateboards" on Google, then later see skateboard ads on Instagram, that’s your data exhaust being used to target you. Note: In advanced studies, you’ll explore how data exhaust is used in machine learning to train AI, sometimes reinforcing biases (e.g., facial recognition working poorly for people of color).

• Behavioral Surplus Definition: The extra data companies collect beyond what they need to improve their service—used to predict and influence your future actions. Example: A weather app doesn’t need to know your political views to tell you it’s raining, but if it collects that data, it can sell it to campaigns trying to sway your vote. Note: In graduate school, you’ll debate whether this is a form of "digital colonialism," where tech companies extract value from users’ lives without fair compensation.

• Dark Patterns Definition: Design tricks used in apps and websites to manipulate you into giving up more data or making choices you wouldn’t otherwise make. Example: A "Free Trial" button that’s bright green and easy to click, while the "Cancel Subscription" option is hidden in tiny gray text at the bottom of the page. Note: In design ethics courses, you’ll analyze how dark patterns exploit cognitive biases (like the "default effect," where people stick with pre-selected options).

3. Assessment Translation

How This Appears on State Assessments (Grade 8): - Multiple Choice: Questions test your ability to identify surveillance capitalism in real-world scenarios (e.g., "Which of these is an example of a company profiting from behavioral surplus?"). Distractors often include: - Confusing data collection with data use (e.g., "A company tracks your location" vs. "A company sells your location data to advertisers"). - Overgeneralizing (e.g., "All free apps sell your data" instead of recognizing that some free apps use ads but don’t sell data). - Short Constructed Response: You might be asked to explain how a specific app (e.g., TikTok, Google Maps) monetizes user data, using at least two key terms from the lesson. - Evidence-Based Writing: A prompt like: "Some people argue that surveillance capitalism is a fair trade—free services in exchange for data. Others say it’s exploitative. Using evidence from the texts, explain which argument you find more convincing." Proficient responses will: - Define surveillance capitalism clearly. - Cite at least two examples of how data is monetized (e.g., targeted ads, selling to third parties). - Address counterarguments (e.g., "Some say users consent to terms of service, but most don’t read them").

Model Proficient Response (Short Constructed Response): Prompt: "Explain how a free mobile game like Candy Crush makes money using surveillance capitalism. Use at least two key terms from the lesson." Response: "Candy Crush is free to play, but it makes money by collecting and selling player data. First, it tracks your data exhaust—like how long you play, which levels you fail, and even how often you tap the screen. This data helps the company predict which players are likely to spend money on in-game purchases. Second, it uses behavioral surplus to target ads. For example, if the game notices you keep failing Level 50, it might show you an ad for a power-up at just the right moment to tempt you to buy it. The company then sells these predictions to advertisers, turning your gameplay into a product."*

What Teachers Look For: - Developing Response: Mentions that the game is "free but has ads" without explaining how data is used or monetized. May confuse ads with data selling. - Proficient Response: Clearly connects data collection to monetization, uses key terms correctly, and provides a specific example. - Advanced Response: Adds nuance, like how dark patterns (e.g., "limited-time offers") manipulate players into spending money, or how this model affects different groups (e.g., kids vs. adults).

4. Mistake Taxonomy

Mistake 1: Confusing "Free" with "No Cost" - Prompt: "Why do companies like Facebook and Instagram offer their services for free?" - Common Wrong Response: "Because they’re nice and want to help people connect." - Why It Loses Credit: Ignores the economic model behind free services. The question is about why companies choose this model, not their intentions. - Correct Approach: 1. Acknowledge that the service is free to use, but not free to provide. 2. Explain that companies make money by collecting and selling user data (e.g., ads, third-party sales). 3. Use an example: "Facebook doesn’t charge users because it makes more money by selling targeted ads based on what you post and like."

Mistake 2: Overgeneralizing Data Collection - Prompt: "True or False: All apps collect and sell your personal data. Explain your answer." - Common Wrong Response: "True, because all apps are part of surveillance capitalism." - Why It Loses Credit: Fails to recognize that some apps (e.g., open-source tools, paid apps) don’t rely on data monetization. The question asks for nuance. - Correct Approach: 1. Start with "It depends." Some apps do, some don’t. 2. Give examples of apps that don’t sell data (e.g., Signal, a privacy-focused messaging app; paid apps like ProtonMail). 3. Explain why: "Apps that charge money or rely on donations don’t need to sell data to make a profit."

Mistake 3: Misidentifying Dark Patterns - Prompt: "Describe a time you’ve encountered a dark pattern online. How did it manipulate your behavior?" - Common Wrong Response: "When an ad pops up and I click it by accident." (This describes an ad, not a dark pattern.) - Why It Loses Credit: Confuses ads with design tricks. Dark patterns are about how the interface is designed to manipulate you, not just the presence of ads. - Correct Approach: 1. Define dark patterns: "Design choices that trick you into doing something you didn’t intend." 2. Give a specific example: "When I tried to cancel a subscription, the ‘Cancel’ button was gray and tiny, while the ‘Keep Subscription’ button was big and green." 3. Explain the manipulation: "The company made it harder to cancel because they wanted me to keep paying."

5. Connection Layer

1. Within Media Literacy-Algorithmic Bias Surveillance capitalism-Algorithmic bias — The same data used to target ads can reinforce stereotypes. If an algorithm learns that women click on more diet ads, it’ll show more diet ads to women, even if they’re not interested, because the system prioritizes profit over fairness.

2. Across Subjects-Economics (Supply and Demand) Your data as a product-Supply and demand — In economics, companies create supply (your data) to meet demand (advertisers’ need for targeted audiences). The more unique or valuable your data (e.g., a CEO’s search history), the higher the price it commands, just like rare collectibles.

3. Outside School-Job Applications Behavioral surplus-Employer screening tools — Some companies use AI to analyze job applicants’ social media or even their facial expressions during video interviews. If you’ve ever posted about mental health or political views, that data could be used to filter you out—without you knowing.

6. The Stretch Question

"If you could design a law to regulate surveillance capitalism, what would it require companies to do—and what would you ban? Would your law allow people to ‘sell’ their own data directly to companies, like a digital farmers’ market? What problems might that create?"

Pointer Toward the Answer: Start by thinking about consent. Right now, most people don’t read terms of service, so they can’t truly consent. A strong law might require companies to: - Explain data collection in plain language (no legal jargon). - Let users opt out of all data selling, not just some. - Pay users a share of the profits if their data is sold (like royalties for artists).

But here’s the catch: If people could sell their own data, would that create a new digital divide? Wealthier people might opt out of selling their data, while lower-income users feel pressured to sell theirs for extra cash. Would that make surveillance capitalism more unfair, not less?