Lesson Report:
**Title:
From Policy “Magic Wands� to Real-World Consequences: Mechanisms, Emergent Effects, and Final Paper Preparation**
In this session, students shifted from thinking of their AI-and-democracy policy proposals as ideal top‑down solutions to examining how those policies would actually operate “on the ground� and what unintended consequences they might generate. Using the smart‑fridge/insurance example and core course readings (Zuboff, Eubanks, DiResta, and black-box algorithm critiques), the class worked in a shared Google Doc to articulate policy mechanisms, identify emergent harms, and begin preparing the analytical foundation for their speculative final narratives.
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### Attendance
– Number of students explicitly mentioned as absent: **0**
(Several students spoke—e.g., Banu, Ermahan, Safi, Elijah, Amin, Muti, Lillian—but no one was marked or mentioned as absent.)
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### Topics Covered (Chronological, with Activities and Examples)
#### 1. Course Logistics, Grading Timeline, and Extra Credit Structure
– **Grade timing**
– Final paper due date reaffirmed as **December 22**.
– Latest date for grade submission: **December 31**, but instructor plans to submit “just after Christmasâ€� (approx. Dec 25–27).
– Clarified that course is nearly finished; **last class meeting is December 11**.
– **Presentation grading**
– Clarified that the **policy proposal presentations** are graded as **class participation**, not as a separate grade category.
– This helps students understand weighting: main graded components are policy memo, critical reflection journals, and speculative final paper.
– **Reflection journals**
– Students asked about the **4th critical reflection journal**:
– One student (Banu) could not find the upload slot on eCourse.
– Instructor confirmed the **4th journal assignment** exists but the upload link was not yet visible; promised to **post it that night**.
– **Extra credit reflection (“5th journalâ€�)**:
– This is **optional** extra credit.
– Task: Revisit their **first reflection journal** and:
– Reassess their original views in light of everything learned since.
– Ask: Would you still agree with your earlier self? What would you add or change?
– Explicitly **connect changes in your opinion to course content** (theories, authors, concepts).
– Successful completion will grant **extra credit equivalent to two reflection journals** in the grade composition.
– Planned due date: **same deadline as the final speculative narrative paper** (Dec 22).
– **Final assignment instructions**
– A student noted the instructor had said instructions would be posted.
– Instructor confirmed that **final assignment instructions** have been posted on eCourse **under the policy memo link**.
– Students were advised to **read the instructions**, but also reassured that **today’s class is designed to make those instructions intelligible** and reduce anxiety.
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#### 2. Reframing Mindset: From Top-Down Policy Design to Ground-Level Human Impact
– **Objective of the day**
– Instructor framed the session’s main goal as completing a **“mindset transitionâ€�**:
– From: Thinking of policy proposals as a **“magic wandâ€�** that fixes a specified problem.
– To: Analyzing what it actually takes for such a policy to **succeed**, and in what ways it may **fail or become problematic**, even if it doesn’t collapse outright.
– **Smart fridge and health insurance premium example (revisited)**
– Image shared in chat: a phone notification about **increased health insurance premiums**.
– Scenario recap:
– Assume the phone belongs to a student (e.g., “this is Ermahan’s handâ€�).
– Notification states that his **health insurance premium increased by $45 per month**.
– Reason: Data from his **smart fridge** shows he’s been consuming a lot of **Ben & Jerry’s ice cream** over the past few months.
– Discussion points:
– **Where did the data come from?**
– Not from hacking or espionage.
– Rather, **Samsung** (fridge manufacturer) is **voluntarily sharing** usage data with the insurance company.
– **Why would Samsung share this data?**
– Students answered: **for money**.
– Insurance companies **purchase** this “fridge behaviorâ€� data.
– With widespread smart-appliance adoption, this becomes a highly **lucrative data market** requiring almost **no marginal cost** to Samsung (surplus data monetization).
– **Why does the insurance company want this data?**
– Also ultimately for **money**, but via **risk management**:
– More ice cream → more sugar intake → higher risk of **diabetes** and other health problems.
– Higher expected **future medical payouts** for that client.
– The insurer uses an **inhumane risk algorithm**:
– Input: Behavioral health proxies (e.g., sugar consumption).
– Output: Adjusted **premiums**—“higher riskâ€� clients pay more upfront.
– This seems *internally rational* from the insurer’s perspective, but raises ethical and distributive concerns.
– **Who benefits and who loses?**
– **Samsung** profits from selling excess data.
– **Insurance company** profits by more precisely pricing risk and shifting costs to higher-risk clients.
– **Consumer (e.g., Ermahan)**:
– **Loses privacy**: routine fridge use becomes a monitored data stream.
– **Pays more** monthly for insurance due to private behavioral data.
– Has little control over these arrangements and unclear recourse.
– Framed as an example of:
– Good intentions (“smartâ€� conveniences) combined with business incentives progressing into **problematic, real-world harms** for ordinary people.
– **Purpose of revisiting this example**
– To push students to **stop thinking solely from the vantage point of government or large firms**.
– To instead emphasize:
– The **human perspective**, i.e., “the person on the groundâ€� experiencing policies’ downstream effects.
– The **second- and third-order consequences** of seemingly benign or even helpful policies and technologies.
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#### 3. Connecting the Example to Course Theory: Zuboff and Surveillance Capitalism
– **Which author predicted the fridge scenario?**
– Instructor challenged the class to identify which course author most directly anticipates this “smart device → data monetization → behavioral risk pricingâ€� scenario.
– Students correctly identified **Shoshana Zuboff (Surveillance Capitalism)**.
– Key concept:
– **“Behavioral surplusâ€� / data surplus**:
– Human behavior captured in digital form beyond what is needed for core service functionality.
– This surplus becomes raw material for predictive products and markets.
– **Core Zuboff ideas reinforced**
– Our everyday actions (clicks, shopping patterns, device usage, etc.) are increasingly:
– **Recorded**, **analyzed**, and **monetized**.
– Used to create **prediction markets**—where our future behaviors, health outcomes, or spending patterns are commodified.
– The **scope of extractable data** is expanding:
– Currently: social media behavior, web clicks, basic device telemetry.
– Future: ambient data from any “smartâ€� connected object—e.g., **fridges, thermostats, cars, health wearables**, etc.
– Tradeoff:
– Users receive **incremental convenience** (shopping lists, sale alerts, reminders).
– They often **unknowingly sacrifice substantial privacy** and autonomy, while firms harvest profit from this hidden surplus.
– **Implication for student assignments**
– Students’ speculative narratives and final analyses must:
– Not only imagine “what could go wrongâ€� in an abstract sense.
– But explicitly **ground those futures** in frameworks from course authors like Zuboff (surveillance capitalism), showing how today’s logics plausibly lead to tomorrow’s harms.
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#### 4. Revisiting Policy Memos: From Utopian Outcomes to Concrete Mechanisms
– **Transition to Google Doc activity**
– Students returned to the **shared Google Doc** used in the previous class.
– Instructions:
– Open your **own tab** (or create one if absent previously).
– Revisit your **policy memo** and the **utopian future scenario** you had already written (how your policy “solvesâ€� an AI-and-democracy problem).
– **New task: articulate the “howâ€� (mechanism)**
– Instructor noted that many prior utopian descriptions focused only on the **end state** (“disinformation is reduced,â€� “democracy is stabilized,â€� etc.).
– Goal for this part:
– Move beyond outcome statements to **mechanistic explanations**:
– **“By what concrete steps does this policy produce that outcome?â€�**
– Example used (Lillian’s proposal):
– Policy: A **French “Digital Electoral Transparency Authorityâ€�** tasked with controlling fake news, particularly **deepfakes**.
– Utopian outcome: “Significant decrease in disinformation media / deepfakes.â€�
– Instructor’s push:
– That’s a good **goal description**, but the narrative needs the **mechanism**:
– How exactly does deepfake labeling work?
– What institutional/technical processes connect policy → outcome?
– **Operationalization concept**
– Instructor introduced the idea of **operationalization**:
– Taking a **high-level policy idea** and specifying the **operational steps**, actors, resources, and tools needed to make it real.
– Generic example mechanism for deepfake control:
– Government funds a **software toolbox** that:
– Continuously **scrapes French media content** online.
– Runs algorithmic tests to **detect deepfakes**.
– Adds flagged content to a **central database**.
– Potentially publishes warnings, labels, or takedown requests via some public channel (e.g., social media accounts).
– This turns an abstract aim (“reduce deepfakesâ€�) into a **plausible, traceable process**.
– **Student work period**
– Students were given ~10 minutes to:
– Take **1–2 of their previously stated utopian outcomes** and write out **how** they are reached.
– Include:
– Who builds or runs needed systems?
– Who funds them?
– Where do the data and labels come from?
– What institutional changes are required?
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#### 5. Clarifying Mechanism Depth: The Deepfake Labeling Example
– **Instructor critiques a sample student mechanism (“Tab2â€�)**
– Student wrote:
– Labeling deepfakes reduces their impact because:
– People are less likely to believe/share content marked as fake.
– Once labeled, a deepfake’s “power to deceiveâ€� largely disappears.
– Exposure reduces incentives for bad actors to create such content.
– Instructor response:
– This is a strong **behavioral argument** (what labeling does in the public sphere), but still lacks **technical/process detail**:
– How are deepfakes *detected* in the first place?
– Is labeling done by **humans, AI software, or both**?
– **Who builds** the detection system? Who funds and governs it?
– How is new posting behavior handled (evasion, new platforms, etc.)?
– **Further elaboration of the “middle boxâ€�**
– Visual framing: Policy (deepfake labeling) → **[mystery middle box]** → Fewer deepfakes.
– Students are expected to define that “mystery middle boxâ€� in their own projects:
– Technology: specific software/algorithms, databases, monitoring processes.
– Institutions: agencies, oversight bodies, legal instruments.
– Resources: budget, staffing, training, infrastructure.
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#### 6. Introducing Four Categories of Negative Emergent Effects (Link to Key Authors)
– **Image/slide presented in class**
– Instructor shared a graphic summarizing **four major types of negative emergent effects** from the course readings.
– “Emergent effectsâ€� defined as:
– **Unintended consequences** that arise when technologies and policies are deployed, especially when real users and incentives interact in complex ways.
– Effects may be **good or bad**; here focus is on problematic ones.
– **Four categories and associated authors**
1. **Surveillance Capitalism (Zuboff)**
– Initial intention:
– Collect user data to **improve services**, personalize experiences, optimize convenience.
– Emergent dynamic:
– Ever-growing **data collection** creates strong incentives to:
– Monetize behavioral surplus.
– Build **prediction markets** around human behavior.
– Data extraction **expands in scope and invasiveness** (e.g., from clicks to fridges to wearables).
– Result:
– Individuals become raw material for **data mining**, often without informed consent, leading to privacy violations and power asymmetries.
2. **Automating Inequality (Eubanks)**
– Initial intention:
– Use algorithms and automation to **increase efficiency**, cut costs, and manage large caseloads (e.g., welfare eligibility, child services).
– Emergent dynamic:
– Automated systems disproportionately **harm the poor and marginalized**:
– Errors, false positives, and rigid rules fall hardest on those with the least resources to contest them.
– “Efficiencyâ€� gains often justify **stricter control and surveillance** of low-income populations.
– Result:
– Technological systems **reproduce and deepen structural inequality**, even when not explicitly designed to discriminate.
3. **Information Overload and Polarization (DiResta)**
– Initial intention:
– The internet and platforms enable unprecedented **information sharing, connection, and learning**.
– Emergent dynamic:
– More information also means far more **noise, confusion, and conflicting narratives**.
– Information abundance enables:
– **Misinformation and disinformation** at scale.
– **Polarization**, echo chambers, and fragmented realities.
– Result:
– Increased information **does not automatically produce better-informed citizens**; it can instead undermine shared facts and public reasoning.
4. **Black Box Decision-Making**
– Initial intention:
– Automate decisions for **speed and efficiency**, handling complex tasks at scale (credit scoring, risk assessments, etc.).
– Emergent dynamic:
– As models become more complex and/or **proprietary**:
– Their internal logic becomes opaque to users and regulators.
– Affected individuals **cannot see or contest** the reasons behind life-altering decisions.
– Result:
– **Opaque power** over individuals with little transparency or accountability, enabling systemic bias and arbitrary harm.
– **Student task with these categories**
– Students were asked to identify **which category best fits their own policy proposal**:
– Is your policy primarily about **data access/surveillance**, **efficiency automation**, **information flow**, or **automated decisions**?
– This classification is meant to:
– Guide which **theoretical framework** they will lean on in their speculative narrative.
– Clarify what **type of emergent harm** they should explore.
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#### 7. Peer Critique: Stress-Testing Policy Mechanisms
– **Reassigning tabs**
– Students were instructed to:
– Move **one tab up** in the shared Google Doc (looping from top to bottom where necessary).
– Work on the **mechanism written by that peer**, not their own.
– **Task 1: Technical criticism of the mechanism**
– Students were to:
– Read the peer’s described mechanism (the “middle boxâ€�).
– Make **one concrete, technical criticism**—
– E.g., “This system would be vulnerable because…â€�
– Or, “A bad actor could circumvent it by…â€�
– Sample instructor reasoning using the deepfake detection toolbox:
– The government toolbox is locked in a **cat-and-mouse game**:
– Deepfake generation tools are **rapidly evolving**.
– Numerous **open-source models** exist (Stable Diffusion, Flux, Z-Image, etc.), which can be tuned to avoid known detection signatures.
– A new cottage industry could arise in France: **“undetectable deepfake servicesâ€�** designed specifically to beat state detectors.
– Insight for students:
– Even well-funded detection systems will be facing **adaptive adversaries**.
– This needs to be acknowledged in their mechanisms and in their speculative outcomes.
– **Task 2: (briefly mentioned at the end) Media/observer response**
– The instructor closed by asking students to think (and add to the doc) about:
– How **media, citizens, or other observers** might respond to their policy in practice.
– What **stories or reports** would emerge when things go wrong or succeed partially.
– This is laying groundwork for narrative perspective in the final assignment (e.g., what would a news article or watchdog report look like in their future world?).
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#### 8. Looking Ahead to Next Week and Final Paper Integration
– **Need for perspective shift**
– Instructor noted that many Google Doc entries are still written in a **third-person, omniscient policy voice**.
– For the **speculative narrative**, students must:
– Adopt **ground-level perspectives** (citizens, affected individuals, journalists, etc.).
– Integrate course theory into **lived experiences** rather than purely abstract analysis.
– **Readings to revisit**
– Students were asked to **re-familiarize themselves** with:
– **Zuboff** – Surveillance capitalism, behavioral surplus, extraction and prediction markets.
– **Eubanks** – Automation in social services and how it punishes the poor.
– **DiResta** – Information flows, disinformation, and polarization.
– The **black-box algorithm** critique author(s) (e.g., Pasquale or similar) – opacity, contestability, and accountability issues.
– Rationale:
– Strong understanding of these texts is **critical** for writing a robust final paper that links speculative scenarios to concrete theoretical frameworks.
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### Actionable Items (for Instructor)
#### High Urgency (Before/By Next Class)
– **Post missing/refined assignments on eCourse**
– Upload the **submission link/instructions for the 4th critical reflection journal** (students reported they could not find it).
– Create and post **clear instructions** for the **extra credit “5th reflection journalâ€�**, including:
– Task description (revisit first journal; compare/contrast; integrate course concepts).
– Rubric or expectations (length, references to readings, etc.).
– Confirmed **deadline: same as speculative final paper (Dec 22)**.
– **Verify final assignment instructions visibility**
– Double-check that the **final speculative narrative/paper instructions** are:
– Accessible under/near the **policy memo link** on eCourse.
– Clearly labeled so students can’t confuse them with past policy memo instructions.
– **Clarify schedule and expectations**
– In the next session or via announcement:
– Reconfirm that **Dec 11 is the last class**.
– Outline what will be done in that final meeting (e.g., integrating perspectives, Q&A on final papers, maybe workshopping examples).
#### Medium Urgency (Before Final Paper Due Date)
– **Connect class activities more explicitly to the “analytical companionâ€� concept**
– The instructor mentioned an “analytical companionâ€� to be discussed at the end of class but did not elaborate.
– In a future session:
– Clarify whether students must write a **formal analytical companion section** alongside their speculative narrative, and:
– How the Google Doc exercise (mechanisms, emergent effects, peer critiques) maps into that component.
– **Support perspective-taking for narratives**
– Provide a brief handout or slide:
– Suggesting **possible narrative perspectives** (e.g., an affected citizen, a caseworker, a journalist, a policy analyst).
– Showing **one brief example** of moving from policy-speak to a grounded narrative scene or vignette.
#### Lower Urgency / Ongoing
– **Participation/presentation records**
– Ensure **policy proposal presentations** are recorded as **participation grades** in the gradebook so that grading is straightforward at term’s end.
– **Monitor and guide use of the shared Google Doc**
– Before the next session, skim student entries to:
– Identify **common misunderstandings** about mechanisms or emergent effects.
– Select 1–2 anonymized examples to **work through in class** as models of strong integration (or instructive failures).
– **Check for technical barriers**
– Ensure all students can:
– Access and edit the shared Google Doc.
– Find all course materials and readings referenced (especially the four key authors used in the emergent effects framework).
Homework Instructions:
ASSIGNMENT #1: Extra Credit Reflection Journal #5 – Connecting Your First Journal to the Course
You will create an additional (optional) reflection journal in which you revisit your very first reflection/video journal and connect your earlier views to what you have learned throughout the course, showing how your thinking has developed in light of the authors and concepts we’ve discussed.
Instructions:
1. **Locate your first reflection journal/video journal.**
– Re‑read (or re‑watch) your first reflection journal from the beginning of the course.
– Pay attention to what topics you addressed, what opinions you expressed, and what assumptions you seemed to hold at that time.
2. **Summarize your original position.**
– In a few sentences, briefly restate what you argued or reflected on in that first journal.
– Identify the key points: What did you think about AI, democracy, surveillance, policy, etc., at that time? What did you seem most concerned or optimistic about?
3. **Reflect on how your thinking has changed (or stayed the same).**
– Ask yourself the questions the instructor posed in class:
– “If I had to make the same reflection journal again on the same topics, would I agree with myself?â€�
– “Would I add or change anything?â€�
– Be explicit: point out specific ideas or sentences from your first journal and explain whether you now:
– still agree and why,
– partly agree but would nuance them, or
– now disagree and why.
4. **Connect your reflection to course content.**
– The professor emphasized that you should “connect it both to how your opinion’s grown and the course content that we’ve dealt with.â€�
– Choose at least two course readings or core concepts and show how they help you reinterpret your first journal. For example, you might draw on:
– **Shoshana Zuboff – Surveillance Capitalism:** being mined for “data surplus,â€� smart devices selling your data (like the smart fridge and health insurance example).
– **Virginia Eubanks – Automating Inequality:** how algorithms designed for “efficiencyâ€� can punish the poor or vulnerable.
Р**Ren̩e DiResta (or equivalent reading on information/disinformation):** how more information online can increase noise, confusion, and polarization.
– **The “black boxâ€� reading on opaque algorithms and automated decision-making:** how algorithmic decisions can become unaccountable and hard to challenge.
– Explain concretely how these ideas would have changed or deepened the way you wrote that first journal.
5. **Describe your learning journey.**
– Reflect on your intellectual growth over the course:
– What have you learned since that first journal that most surprised you?
– Which concepts made you rethink your earlier assumptions?
– Are there new ethical or political concerns you didn’t see before?
– Make this personal and specific rather than generic; refer to particular class discussions (e.g., the smart fridge and insurance example, deepfake labeling debates, policy memo work) that shifted your perspective.
6. **Write the new reflection journal.**
– Compose a new reflection that:
– briefly summarizes your original stance,
– analyzes how your thinking has evolved, and
– explicitly weaves in course authors and concepts to explain that evolution.
– Aim to **match the general length and format of your previous reflection journals** (unless different requirements are specified in the assignment description on the course page).
7. **Make clear that this is Journal #5 (Extra Credit).**
– Title your work clearly so it’s distinguishable from your earlier journals, e.g., “Critical Reflection Journal 5 – Extra Credit: Revisiting Journal 1.â€�
– Indicate in a short note at the top that this is an extra-credit journal linking back to your first journal.
8. **Check alignment with the grading purpose.**
– Remember: if “you are able to do that, then you will have two reflection journals worth of extra credit added to that section of the grade composition.â€�
– Before submitting, confirm that your journal:
– explicitly compares old and new views, and
– explicitly connects to course readings and ideas (not just vague statements like “I learned a lotâ€�).
9. **Submit by the final paper deadline.**
– The instructor indicated that the deadline for this extra-credit journal “will probably be the same day as the… speculative narrative final paper,â€� and that you should “just submit it at the same time and you’ll be good.â€�
– Upload your journal to the correct submission area once it is posted, by the same due date as your final speculative narrative assignment.
ASSIGNMENT #2: Review Core Course Texts on Emergent Effects for Final Paper Preparation
You will review four key course texts/concepts—the ones mapped in the “negative emergent effects� image discussed in class—to solidify your understanding of how well‑intentioned technologies and policies can lead to unintended harms. This review is meant to strengthen the theoretical foundation for your final speculative narrative paper and for the policy analysis work we will continue next week.
Instructions:
1. **Identify the four core texts/concepts.**
Revisit the readings that correspond to the four quadrants the instructor described:
1. **Surveillance Capitalism (Zuboff):** services that collect data for “improvement� but turn your life into extractable, profitable data surplus (e.g., smart fridges sharing data with insurance companies).
2. **Automating Inequality (Eubanks):** algorithms designed for efficiency that end up disproportionately punishing poor and marginalized people.
3. **Information/Disinformation & Polarization (e.g., DiResta):** online information systems built to “share information� and “connect� that instead create noise, confusion, and polarization.
4. **Black-Box Algorithms & Opaque Decision-Making:** systems that automate decisions for efficiency but become opaque, unaccountable “black boxes� whose decisions are hard to understand or challenge.
2. **Re-read or closely skim each text.**
– For each reading, refresh your memory on:
– the author’s main argument,
– the key mechanisms they describe (how we get from “good intentionâ€� to “bad emergent effectâ€�), and
– any concrete examples or case studies (e.g., smart devices selling data, welfare algorithms, content recommendation systems, automated risk scores).
– Focus your attention on the parts that relate most clearly to your own policy memo and speculative narrative topic.
3. **Clarify the “good intention → emergent harm� chain for each author.**
For each text, write brief notes (for yourself) that answer:
– What was the **initial goal** or promise of the technology/policy (e.g., convenience, efficiency, better information, fairness)?
– What **data or automation** practices were introduced to realize that goal?
– What **unintended negative effects** emerged (e.g., data extraction and surveillance, punishment of the poor, increased polarization, opaque and unappealable decisions)?
– How do these effects show up in real people’s lives (like the fridge–insurance example where only the user loses while Samsung and the insurer profit)?
4. **Connect each text to your own policy proposal.**
– Identify which of the four categories your policy idea primarily fits into, as the instructor asked you to do in the Google Doc:
– Does it increase or regulate access to user data?
– Does it aim at efficiency/automation of services?
– Does it focus on information flows and content moderation/disinformation?
– Does it introduce or govern automated decision-making systems?
– For your main category, note down specifically:
– How your proposed mechanism is supposed to work (the “middleâ€� step you developed in class).
– Which author’s warnings are **most relevant** to what might go wrong with your policy.
5. **Re-examine the smart-fridge/insurance example as a model.**
– Use the in-class example to structure your thinking:
– Start from a seemingly beneficial service (smart fridge convenience).
– Trace how data collection led to data sharing and monetization (Samsung selling data to insurers).
– See how the emergent effect harms the individual (higher insurance premiums, loss of privacy) while both companies profit.
– Practice doing something similar for your own policy: imagine how a company, government, or “bad actorâ€� might use or twist your mechanism in a way that reflects the concerns of Zuboff, Eubanks, DiResta, or the black‑box reading.
6. **Prepare to use these texts explicitly in your final paper.**
– As you review, highlight or note down a small number of **key quotations or concepts** from each relevant author that you might want to cite in your speculative narrative assignment.
– Focus on material that helps you:
– describe what goes wrong in your imagined future scenario, and
– connect that failure to patterns already identified by our authors (surveillance capitalism, automating inequality, information disorder, black‑box opacity).
7. **Bring this understanding to the next class.**
– The instructor indicated that this review is important because “understanding these core concepts from these texts is going to be really important to doing a strong job on this paper,â€� and that next week you will “wrap this upâ€� by integrating these perspectives more fully.
– Come to the next lesson ready to:
– discuss which category your policy falls into, and
– explain how one or more of these authors would critique the emergent effects of your policy in practice.
8. **Note: there is no separate submission for this review.**
– This is preparatory work to support your ongoing Google Doc exercises and your final speculative narrative paper.
– Keep your notes for your own use; you will draw on them both in class and when drafting/revising your final assignment.