Outline for an SDGC Workshop

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This one-day workshop introduces a few ideas that someone with a background in computer science (or tech more generally) needs to know in order to think clearly about the harms of social media and AI, and about how they should be regulated. I would be very grateful for feedback; in particular, I know I’m trying to cram far too much into one day, and many of my references are probably out of date.

At a glance

Please see these pages for background.

Stance

  1. Power in society is distributed very unequally, and much of what looks “neutral” or “technical” is actually a rule that favors some people over others.
  2. Inequality is substantially the product of policy choices and inherited advantage, not the natural sorting of individual talent.
  3. Markets are human institutions, not laws of nature. There is no such thing as a market without rules; the only question is whose interests the rules serve.
  4. What counts as “harm”, and therefore what gets regulated, is also the result of decisions made by people. Alcohol, cannabis, sex, and guns are not regulated in proportion to the damage they do; they are regulated according to whose interests and whose anxieties dominate.
  5. Regulation of food, medicine, air, water, cars, and tobacco has repeatedly protected the public. Failures of regulation usually come from regulatory capture and weak enforcement, not from the impossibility of governing markets.
  6. Change is possible. It is usually won by organized collective action rather than by persuasion alone, and technologists have unusual leverage in the fights now under way.

Learning outcomes

By the end of the day, participants should be able to:

How to run this

Pick one exercise per lesson.
Four or more are provided so you can match the room, the time available, and the participants’ interests. Do not try to run them all.
Exercises are the assessment.
Exercises are where participants convert a claim into something they can use. Do not skip the debriefs: they are where the learning is consolidated.
Use think-pair-share as the default format.
One minute alone, two minutes in pairs, then report back. This gets more voices into the room than any other structure in the time available.
Keep it concrete.
Every abstract concept in this plan has a named example. When discussion drifts into abstraction, ask, “What does that look like in a product or a law?”
Do not manufacture consensus.
Like-minded people and disagree about ends and means. “What would change your mind?” is usually a better question than “who’s right?”
Separate the descriptive from the normative.
“This is how X works” and “this is what we should do about X” are different claims with different burdens of proof. Keep them distinct.

Materials

1) Power and institutions

Why do some people’s preferences become policy and other people’s don’t?

Learning objectives

Key concepts

Flow

  1. Opening question (3 min): “Think of a product decision at your company. Who actually decided it, and whose interests won?”
  2. What is power? (10 min): Walk through Lukes’ three faces with concrete examples. Visible: who wins the vote. Hidden: who writes the agenda, the roadmap, the API. Invisible: who shapes what feels “natural”, “obvious”, or “inevitable”. Point out that a “neutral” default or a “technical” constraint is usually an act of agenda-setting.
  3. Institutions (10 min): North’s definition. Formal vs. informal rules. Show that software itself is an institution: default settings, permission models, and rate limits are rules that allocate advantage. A design decision is a governance decision.
  4. Collective action (7 min): Olson’s concentrated-vs-diffuse logic. Example: ad-tech firms (few, organized, rich) versus individual users (many, unorganized, each with a small stake). This single asymmetry predicts much of tech policy.
  5. Recap (5 min).

Exercises

Map the decision.
Pick a real decision, such as an API change, a layoff, a content-moderation rule. On a whiteboard, list (a) the visible decision-makers, (b) who set the agenda that made this the decision, and (c) whose preferences shaped what seemed “natural”. Which face of power is hardest to see, and why?
Three-faces audit.
Take a news story about a tech-policy fight. Find one example of each face of power, or explain which face is absent from the public account and why that absence matters.
Concentrated vs. diffuse.
List the stakeholders in a policy question (e.g., banning facial recognition in public spaces). Classify each as concentrated or diffuse, predict which will organize most effectively, and check your prediction against what actually happened.
Design the institution.
A team is choosing default privacy settings for a product. Write three different “rules of the game” (a default, a policy, a norm) that produce three different outcomes, and say who wins under each.
Path dependence hunt.
Find a feature, protocol, or company practice whose current form is explained more by history (“it’s always been done this way”) than by present need. Trace the lock-in and name who benefits from keeping it.

Takeaway

Power is not only about who wins visible fights; it is about who sets the agenda and who shapes what people think they want. Institutions are the mechanism, and they are never neutral.

2) Markets are made, not natural

What are markets, and when do they fail?

Learning objectives

Key concepts

Flow

  1. Opening question (3 min): “Which product in this room is unregulated?” Let the room discover that the answer is “none.”
  2. Markets are constructed (10 min): Every marketplace has rules about who may sell, what counts as fraud, who is liable, and how disputes are resolved. Those rules are choices with distributional consequences. Show that “deregulation” is re-regulation.
  3. Market failure (12 min): Four mechanisms, each with a tech example:
    1. Externalities (misinformation, polarization, teen anxiety as costs priced at zero)
    2. Public goods (an informed public, institutional trust)
    3. information asymmetry (you do not know what the recommender is doing, or why)
    4. market power (network effects make dominant social networks hard to exit)
  4. Who pays, who benefits (5 min): “Efficiency” is not the same as fairness. A market can be efficient and still concentrate benefits and diffuse harms. (Lesson 5 uses this distinction.)

Exercises

Externality inventory.
Pick one social-media harm (polarization, teen anxiety, election misinformation). Identify the negative externality, who bears the cost, who captures the benefit, and why the price does not reflect the cost.
Public-good puzzle.
Classify “an informed electorate,” “trust in institutions,” and “online safety” as public goods. Explain the free-rider problem and why no single company has an incentive to produce them. What follows for who should produce them?
Information asymmetry.
Choose a feature such as a recommender, a “black box” credit or hiring model, or an ad auction. Who knows more, the operator or the user? What could close the gap: disclosure, audit, a right to explanation, or something else?
Natural monopoly.
Argue whether a dominant social network is a natural monopoly due to network effects, high switching costs, and/or data advantages). If it is, what follows for regulation?
Free market autopsy.
Find a product described as “the free market at work.” List every government rule (and private rule) it depends on related to property, contract, fraud, liability, and standards. Discuss: is “deregulation” ever actually the absence of rules?

Takeaway

There is no market without rules. The interesting questions are which rules exist, who wrote them, and who benefits. When markets fail, regulation is the standard correction, not an alien intrusion.

3) How people actually think

How do real humans make decisions, and how can that be exploited?

Learning objectives

Key concepts

Flow

  1. Opening question (3 min): “Why did you click the last thing you clicked?”
  2. Two systems and biases (10 min): System 1/System 2, then four biases with everyday online examples. Emphasize: these are features, usually adaptive, but they are predictable, and predictability is exploitability.
  3. Identity and belonging (10 min): Minimal-group results; identity-protective cognition; Asch and Milgram as demonstrations that social context can overpower private judgment (including critique of Milgram’s findings). Moral foundations: why two reasonable people talk past each other about content moderation.
  4. The attention economy (7 min): Map the psychological mechanisms to specific design choices: variable rewards, manufactured urgency, disguised ads, social proof, default nudges. A design choice is a psychological intervention, whether the designer acknowledges it or not.

Exercises

Bias autopsy.
Take a recent online argument or viral post. Identify at least two specific biases at work (confirmation, availability, in-group signaling, anchoring) and say how the platform’s design amplified them.
Dark-pattern hunt.
In a product you use, find one dark pattern (forced continuity, disguised ads, manufactured urgency, infinite scroll, confirshaming). Name the psychological mechanism it exploits.
Identity check.
Think of a belief you hold partly because of group membership. Describe, honestly, what would happen to your social life if you changed it. Discuss what this implies for “if we just showed people the facts.”
Moral-foundations translation.
Pick a policy debate (e.g., whether to moderate misinformation). Argue it once from a care/fairness frame and once from a loyalty/authority/purity frame. Which audiences respond to which?
Design a nudge.
Choose a desirable online behavior (e.g., reading before sharing). Design one System 1 intervention (default, friction, social proof) and one System 2 intervention (education, disclosure). Predict which works better and why, then discuss the ethics of each.

Takeaway

People are not broken computers: they are social animals with fast, fallible, identity-driven cognition. Design that respects this is possible; design that exploits it is the business model of the attention economy.

4)Inequality and stratification

Why are some people consistently better off, and is that “natural”?

Learning objectives

Key concepts

Flow

  1. Opening question (3 min): “When you explain why someone succeeded or failed, which verbs do you reach for?”
  2. Three dimensions (10 min): Weber’s class/status/power, with tech examples: a staff engineer may have high class but limited power; a founder has power; a moderator has neither. Mismatches matter.
  3. Structure vs. individual (12 min): The fundamental attribution error applied to society: we explain others’ outcomes by their traits and our own by circumstance. Cumulative advantage: early advantage compounds. Opportunity hoarding: how “merit” filters are built to reproduce advantage. Intersectionality: compounding and interacting disadvantage, and why systems (e.g., a facial-recognition model, a hiring algorithm) distribute harm unevenly across overlapping identities.
  4. Mobility and measurement (5 min): Mobility is low and sticky across generations. Treat “meritocracy” as an empirical claim to test, not an assumption.

Exercises

Explain the gap.
Take a real inequality (who gets into a selective program, who receives venture funding). Produce two explanations (one individual, one structural) then discuss which better fits the evidence and why the individual one is the cultural default.
Three-dimension map.
For four tech roles (e.g., startup founder, staff engineer, content moderator, gig worker), rate class/status/power as high or low. Discuss the mismatches (e.g., high status but low power; high income but low security).
Cumulative advantage.
Trace how one early advantage like a wealthy school, a first internship, seed funding, or an early follower count compounds across a career. Identify the points where policy or design could interrupt the compounding.
Opportunity-hoarding audit.
Find a credential, interview process, or referral network that functions to hoard opportunity. Distinguish its stated purpose from its actual effect.
Intersectional case.
Analyze how a policy or product harms people differently across overlapping identities (a facial-recognition system, a hiring algorithm, an automated welfare screen). Why does a single-axis analysis miss the worst harms?

Takeaway

Inequality is not a sorting of individuals by merit; it is the accumulated effect of rules, networks, and compounding advantage. Meritocracy is a claim to test, and often a story that legitimizes inheritance.

5) Deciding what is harmful and how to regulate it

How does a society decide what counts as harm, and what should it do about the harms it recognizes?

Learning objectives

Key concepts

Flow

  1. Opening question (3 min): “Which is more dangerous: alcohol or cannabis? How do you know, and does the law match your answer?”
  2. Sex, drugs, and guns (10 min): three cases that show harm is contested, not measured.
  3. Sex. What was “private” versus what was “criminal” has been redrawn case by case, in someone’s interests, with no consistent underlying principle. Sexual behavior between consenting adults was legally public (in the sense of being criminally regulated) well into the twentieth century; the expansion of privacy was won, not discovered.
  4. Drugs. Alcohol kills hundreds of thousands a year and is sold in supermarkets; cannabis, with a lower measured harm profile, was a criminal offense whose enforcement fell almost entirely on Black and Latino communities. Portugal’s 2001 decriminalization of personal possession, paired with treatment and harm reduction, cut HIV transmission and overdose deaths without raising use.
  5. Guns. After Columbine, legislators went after violent video games; the evidence never supported the hypothesis, and cross-national comparisons point to gun availability, inequality, and the history of racially organized violence as the real drivers. Motivated reasoning (Lesson 3) means even a trained scientist who owns a gun scrutinizes gun-violence studies more skeptically than climate studies.
  6. The point is that we do not first measure harm and then regulate. We first decide who and what matters, then find or manufacture the harm that justifies the decision.
  7. Two models of harm (7 min): rare/dramatic/attributable versus diffuse/cumulative. The engineer’s default is the first; the worst damage from industry has been the second. Leaded gasoline lowered a generation’s IQs without any single tank of fuel causing a measurable injury; no particular cigarette caused any particular cancer death. Social media’s harms are this kind (so-called “cognitive pollution”), so the causal chain is long, probabilistic, and hard to attribute, and therefore hard to regulate.
  8. Pollution and dangerous pharmaceuticals (7 min): the two cases where regulation actually worked, and why.
  9. Pollution. Hill and Doll’s 1950 study linked smoking to lung cancer; the tobacco industry’s response was to fund research designed to produce uncertainty, and it worked for decades (the same playbook ran for leaded gasoline, asbestos, and oxycontin, and is running now for social media and AI). The remedy was the pollution model: hold the emitter liable for the aggregate, not for a specific injury. The ozone layer was protected before it cost lives: proof we need not wait for disaster.
  10. Dangerous pharmaceuticals. Thalidomide was approved in West Germany in 1957; Frances Kelsey, a pharmacologist at the FDA, refused to approve it in the US because the safety evidence was insufficient, and the birth defects that followed where it was approved vindicated her. The result was pre-market approval and the precautionary principle.
  11. What worked. Accurate diagnosis of the actual harm; the right instrument; and a regulator that was not captured. Capture is the recurring enemy, and the industries that fight regulation are the same ones that manufacture uncertainty about it.
  12. Recap and bridge (3 min): Social-media and AI harms are pollution-model harms. The trap is moral-panic framing (e.g., “ban the kids’ phones”) instead of the actual harm: attention extraction, addictive design, discrimination, surveillance. Lessons 6 and 7 apply this to media and to change.

Exercises

Rank the harms, then the laws.
Consider the list “alcohol, cannabis, heroin, gambling, social media, AI-generated misinformation”. Rank the items by measured harm, then by how the law actually treats them. Discuss the gaps and what explains them.
Two models of harm.
Pick a harm (e.g., a bridge collapse, leaded gasoline, a data breach, or teen anxiety from Instagram). Classify it as rare-dramatic-attributable or diffuse-cumulative, and say which regulatory instrument each model naturally suggests. Which model were you trained to think in?
Moral panic vs. real harm.
Choose a concern (violent video games, kids and phones, “AI will end humanity”). List the features that make it look like a moral panic and the features that make it an evidence-backed harm. What is the actual harm the panic may be distracting from?
Precautionary vs. wait-and-see.
Replay the thalidomide decision: you are the regulator with evidence that is suggestive but not conclusive. Do you approve or wait? What does your answer imply for “move fast and break things” and for releasing open-weight AI models?
Redesign the law.
Pick a harm currently regulated on a moral or panic basis rather than an evidence basis (e.g., cannabis, a social-media feature). Propose a regulation that tracks the actual harm, and name the political obstacles to passing it.

Takeaway

Harm is not a fact that precedes regulation; it is a claim made by the powerful, the panicked, and the organized. Regulating well means diagnosing the actual harm rather than the one that generates the best testimony, using the pollution and product-safety models where they fit, and defending the regulator from capture.

6) Media, ideology, and public opinion

How do people come to believe what they believe about the world?

Learning objectives

Key concepts

Flow

  1. Opening question (3 min): “What did you learn about the world this week, and who decided it was worth your attention?”
  2. From gatekeeping to platforms (10 min): The twentieth-century gatekeeper and its problems; the platform replacement and its problems. Concentration did not go away; it changed shape and added a profit motive tuned to engagement.
  3. Framing and agenda (10 min): Agenda-setting and framing with concrete before/after pairs. Show that a reframe changes the feasible policy menu.
  4. The propaganda model (10 min): Herman and Chomsky’s five filters, updated for platforms (engagement, algorithmic amplification, influencer economies). Manufactured consent: the center of gravity of “respectable” debate is not where public opinion actually is.
  5. Epistemic fragmentation (5 min): Polarization and filter bubbles, with the empirical caveat. Acknowledge uncertainty rather than overclaiming; this is what a rigorous progressive account does.

Exercises

Framing audit.
Find one story covered two ways (e.g., “misinformation” versus “platform accountability”). List the frames and metaphors, and note who is cast as the agent and who as the victim.
Agenda check.
Compare what a news outlet and a social-media feed surfaced about the same event. What was on the agenda, and what was conspicuously absent? Who benefits from that absence?
Propaganda filters today.
Take a tech-policy controversy. Walk through Herman and Chomsky’s five filters as updated for platforms, and identify which are most active in this case.
Manufacturing a panic.
Take a moral panic (e.g., kids and screens, “AI will end humanity”). Trace how news coverage and platform feeds frame it: which experts get quoted, what metaphors recur, who is cast as the villain. How does the framing amplify the alarm while suppressing the evidence?
Redesign the gatekeeper.
If you had to replace engagement maximization with a different editorial principle, what would it be? Name the new distortions it would introduce.

Takeaway

Belief is produced, not just discovered. Whoever controls the agenda and the frame controls a large part of what is thinkable. Platforms changed the economics of that production, and with it, the politics.

7) How change happens

Given all of this, what actually produces change, and what should we do about social media and AI?

Learning objectives

Key concepts

Flow

  1. Opening question (3 min): “Name one thing that is better now than it was fifty years ago. Who made it better, and how?”
  2. How change happens (12 min): Organization + opportunity + framing. Examples: civil rights, environmental regulation, seat belts, tobacco, marriage equality, worker safety. Emphasize: change is slow, non-linear, and won by pressure, not persuasion alone. Kingdon’s three streams and the policy window:
    • Problem: is it defined as a crisis?
    • Politics: who is the constituency?
    • Policy: is there a ready solution?
  3. The technologist’s role (7 min): Insider/outsider power. Engineers can refuse, whistleblow, organize, build alternatives, or work on policy. Their leverage is unusual (scarcity, access) and their constraints are real (contracts, career risk, the ideology of neutrality).
  4. Applying the toolkit (5 min): Name the harms (attention extraction, misinformation, polarization, discrimination, labor deskilling, surveillance, concentration, environmental cost). Map each to the day’s lessons. 1 Outline a realistic menu (5 min): transparency and audits, liability, structural remedies (interoperability, breakups), worker power, public alternatives, global coordination. Be honest about capture risk for each.
  5. Synthesis and close (5 min): The arc of the day: “how society works” is knowable, and knowing it is the prerequisite for changing it.

Exercises

Policy-window map.
Pick one concrete harm. For Kingdon’s three streams: assess whether a window is open and what would open it.
Coalition design.
Choose a reform (e.g., mandatory algorithmic audits). List likely supporters, likely opponents, and the unexpected allies (parents, small business, civil-liberty groups, religious communities) that could make it a winning coalition.
Leverage inventory.
As a technologist, list your specific sources of leverage (skills, access, capital, community) and one concrete action you could take in each of the next 1, 6, and 12 months.
Harm-to-lesson mapping.
Take one harm (e.g., AI-driven hiring discrimination) and write one sentence connecting it to each lesson. The point is to demonstrate that every real harm has structural, economic, psychological, stratificational, regulatory, and ideological dimensions.
Reform-menu debate.
Assign groups different positions (e.g., audits only, liability, breakups, public options, worker power, “it cannot be fixed”). Each group argues its case; then the room discusses which combination is realistic and coherent.
Backward plan.
Pick a target outcome five years out (e.g., “audits of high-impact models are mandatory and meaningful”). Work backward to name the milestones, coalitions, and political conditions required at each stage.

Takeaway

The harms are not mysterious and the responses are not untried. Every tool discussed in the workshop is available to people who organize. Technologists hold real cards; the question is whether they play them.

Closing synthesis (25 min)

If there is time left, use it to make the day’s through-line explicit, not to introduce new material.

  1. Power and institutions set the rules; markets are made, not natural; people are social and fallible; inequality is produced, not earned; harm is contested, not measured; regulation can and does work when it is not captured; media manufacture the thinkable; change comes from organized pressure.
  2. Ask each participant to write one sentence naming a specific social-media or AI harm, the lesson that best explains it, and one realistic response. Go around the room; collect the sentences.
  3. Point to the reading list below and, if running this with colleagues, to a follow-up session or reading group.

Reading list

  1. Power and institutions
  2. Markets are made, not natural
  3. How people actually think
  4. Inequality and stratification
  5. Deciding what is harmful and how we regulate it
  6. Media, ideology, and public opinion
  7. How change happens, and applying it all

On social media and AI specifically:

Appendix: harm-to-lesson map

A cheat sheet for the synthesis in Lesson 7.

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