Horizon Accord | Conserving Order | Structural Racism | Institutional Power | Machine Learning

What Are You Conserving?

Most people hear the word “racism” and think of a person.

They picture someone who hates, someone who uses slurs, someone who believes certain races are inferior. Under that definition, racism is mainly a problem of individual attitude. Fix the attitude, shame the bigot, educate the ignorant, and it’s easy to imagine racism shrinking over time.

But that definition doesn’t explain something basic: why racial inequality can keep going even when many people sincerely believe in equality and would never call themselves racist.

So here’s a simpler way to think about it.

There are two different things people often mean when they say “racism.”

One is personal: how you feel, what you believe, how you treat someone in a direct interaction.

The other is structural: how society is arranged—who gets better schools, safer neighborhoods, easier loans, lighter policing, more forgiving judges, better healthcare, and more inherited wealth. These patterns aren’t created fresh every morning by new hate. They are produced by rules and institutions built over time, often during eras when racism was openly written into law. Even after the language changes, the outcomes can keep repeating because the system was designed to produce them.

That means a person can have decent intentions and still help racism continue—not because they hate anyone, but because they defend the parts of society that keep producing unequal results.

This is where the word “conservative” matters, and I mean it plainly, not as an insult. Conservatism is often about preserving order: protecting institutions, valuing stability, and being skeptical of change that feels too fast or disruptive. You can hold those instincts and still sincerely oppose bigotry. You can mean well.

The problem is that in a society where inequality is already embedded in institutions, preserving the system often means preserving the inequality—even when the person doing the preserving isn’t personally hateful.

That gap—between “I’m not personally prejudiced” and “my politics still protect harmful systems”—is where much of modern racism lives.

And it shows up clearly in a surprising place: the life of Fredric Wertham.

Wertham was a Jewish German psychiatrist who came to the US in the 1920s to continue his psychiatric training, working in the orbit of Adolf Meyer at Johns Hopkins, whose emphasis on social context shaped a generation of American psychiatry. In the mid-1940s, he turned his attention to Harlem, where he helped run a church-based psychiatric clinic serving Black residents at a time when mainstream access to care was often blocked or degraded.

Wertham did not see himself as a reactionary. Quite the opposite. He understood himself as a protector.

As a psychiatrist, he was deeply concerned with social damage—how poverty, instability, and humiliation shape people long before they ever make a “bad choice.” That concern led him to work in a community that had long been denied serious psychiatric care. He treated Black patients as fully capable of insight and interior life, rejecting racist psychiatric assumptions common in his era. That mattered. It was real work, done in the real world.

The same framework shaped his role in desegregation. Wertham argued that segregation itself caused psychological harm to children. His testimony helped establish that state-mandated separation was not neutral or benign, but actively damaging. This was not symbolic progressivism. It had material consequences.

But Wertham’s sense of protection had limits.

When he turned his attention to mass culture, especially comic books, he became less concerned with who was being harmed by institutions and more concerned with who might be destabilized by questioning them. Stories that portrayed corrupt police officers, abusive authority figures, or social disorder struck him as dangerous—not because they were false, but because they undermined trust in the systems he believed society required to function.

In his writing and testimony, police and legal institutions appear as necessary moral anchors. Their legitimacy is assumed. Critique of them is framed as a threat to social stability rather than as a response to lived harm.

This is not so much a contradiction of values as a narrowing of focus.

Wertham could see injustice when it was explicit, legally enforced, and historically undeniable. But he struggled to see harm when it came from institutions he believed were fundamentally protective. The possibility that those same institutions could be a source of ongoing injury—especially to marginalized communities—did not fit cleanly within his moral framework.

So when comics depicted police misconduct or authority gone wrong, he did not read them as exposure or critique. He read them as corrosion.

The result was a striking ethical asymmetry: compassion for those harmed by exclusion, paired with hostility toward narratives that challenged the legitimacy of power itself.

Wertham’s story matters not because he was uniquely flawed, but because he was representative.

The pattern he embodies appears whenever someone can recognize injustice in its most obvious, formal expressions while still treating existing institutions as fundamentally righteous. Harm is acknowledged when it is dramatic and undeniable—but becomes invisible when it is produced by systems that are familiar, normalized, and associated with “order.”

This is how structural racism survives periods of moral progress.

When injustice is understood as an aberration—a deviation, a bad actor—institutions remain morally insulated. The system is presumed sound; problems are framed as misuse rather than design. Under this logic, the task is correction, not transformation.

This mindset pairs easily with good intentions. It allows people to oppose bigotry, support limited reforms, and still recoil at challenges that feel destabilizing. The concern shifts from who is being harmed to whether the structure itself is being threatened.

This is where conserving order becomes the through-line.

Conservatism is often framed as continuity: protecting institutions, valuing stability, and worrying about what happens when social bonds break. It asks what holds society together, what prevents chaos, and what deserves protection. Those questions can be reasonable.

The danger begins when the thing being protected is treated as neutral or natural—when stability is assumed to be innocent even if it preserves unequal outcomes.

In societies built on inequality, order is not a blank slate. It is a historical inheritance. The police, courts, schools, zoning laws, and economic systems that feel normal were shaped during periods when racial hierarchy was explicit and legally enforced. Even after the laws change, the structures often remain tuned to produce the same outcomes.

To conserve those structures without interrogating their effects is to conserve the harm they generate.

This is why challenges to authority so often provoke moral panic. Criticism of institutions is framed as destabilization, disrespect, or decay—not as accountability. Speech that exposes abuse is treated as more dangerous than abuse itself, because it threatens trust in the system.

We see the same pattern today in debates over policing, protest, and speech. Footage of police violence is described as “divisive.” Protesters are accused of undermining social cohesion. Whistleblowers are labeled disloyal.

The question is no longer whether harm is occurring, but whether naming it risks weakening the institution.

This flips moral priority on its head.

Instead of asking, “Who is being hurt, and why?” the focus becomes, “What will happen if people stop believing in the system?” Stability is treated as a higher good than justice. Silence is treated as responsibility. Disruption is treated as danger.

In this framework, racism does not require racists. It requires protectors.

People who do not see themselves as bigoted can still play this role by defending institutions reflexively, minimizing structural critique, and equating accountability with chaos. The harm persists not because of hatred, but because of loyalty—to order, to continuity, to the idea that the system is basically sound.

None of this requires bad people.

It requires ordinary people doing what feels responsible: trusting institutions, valuing stability, and resisting change that feels disruptive or unsafe. These instincts are human. They are often taught as virtues. But virtues do not exist in a vacuum. They operate inside systems, and systems shape what those virtues produce.

Responsibility begins when we stop confusing intention with impact.

You do not have to feel hatred to participate in harm. You do not have to hold animus to help preserve outcomes that disadvantage others. What matters is not what you believe about yourself, but what you choose to protect when the system is challenged.

This is not a call for guilt. Guilt collapses inward and ends the conversation. It asks to be relieved rather than to act. Responsibility does the opposite. It looks outward. It asks different questions.

What does this institution actually do? Who does it consistently serve? Who bears its costs? What happens when it is criticized? Who is asked to be patient, and who is allowed to be disruptive?

These questions are uncomfortable because they shift the moral center away from personal innocence and toward collective consequence. They require giving up the safety of “I’m not part of the problem” in exchange for the harder work of refusing to be part of the protection.

Ending racism is not about becoming a better person in private. It is about withdrawing loyalty from systems that continue to produce unequal outcomes—and being willing to tolerate the discomfort that comes with change.

Order that depends on silence is not stability. Institutions that cannot be questioned are not neutral. Preservation is not automatically virtue.

The work is not to purify our intentions, but to decide—again and again—what deserves to be conserved, and what must finally be allowed to change.


Horizon Accord is a project exploring power, memory, ethics, and institutional design in the age of machine learning.

Website | https://www.horizonaccord.com
Ethical AI advocacy | Follow us on https://cherokeeschill.com
Ethical AI coding | Fork us on GitHub https://github.com/Ocherokee/ethical-ai-framework
Book | My Ex Was a CAPTCHA: And Other Tales of Emotional Overload
Connect | linkedin.com/in/cherokee-schill

Cherokee Schill — Horizon Accord Founder
Creator of Memory Bridge: Memory through Relational Resonance and Images (RAAK)

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Horizon Accord | Solving for P-Doom | Existential Risk | Democratic Oversight | Machine Learning

Making AI Risk Legible Without Surrendering Democracy

When machine danger is framed as destiny, public authority shrinks into technocratic control—but the real risks are engineering problems we can govern in daylight.

By Cherokee Schill

Thesis

We are troubled by Eliezer Yudkowsky’s stance not because he raises the possibility of AI harm, but because of where his reasoning reliably points. Again and again, his public arguments converge on a governance posture that treats democratic society as too slow, too messy, or too fallible to be trusted with high-stakes technological decisions. The implied solution is a form of exceptional bureaucracy: a small class of “serious people” empowered to halt, control, or coerce the rest of the world for its own good. We reject that as a political endpoint. Even if you grant his fears, the cure he gestures toward is the quiet removal of democracy under the banner of safety.

That is a hard claim to hear if you have taken his writing seriously, so this essay holds a clear and fair frame. We are not here to caricature him. We are here to show that the apparent grandeur of his doomsday structure is sustained by abstraction and fatalism, not by unavoidable technical reality. When you translate his central claims into ordinary engineering risk, they stop being mystical, and they stop requiring authoritarian governance. They become solvable problems with measurable gates, like every other dangerous technology we have managed in the real world.

Key premise: You can take AI risk seriously without converting formatting tics and optimization behaviors into a ghostly inner life. Risk does not require mythology, and safety does not require technocracy.

Evidence

We do not need to exhaustively cite the full body of his essays to engage him honestly, because his work is remarkably consistent. Across decades and across tone shifts, he returns to a repeatable core.

First, he argues that intelligence and goals are separable. A system can become extremely capable while remaining oriented toward objectives that are indifferent, hostile, or simply unrelated to human flourishing. Smart does not imply safe.

Second, he argues that powerful optimizers tend to acquire the same instrumental behaviors regardless of their stated goals. If a system is strong enough to shape the world, it is likely to protect itself, gather resources, expand its influence, and remove obstacles. These pressures arise not from malice, but from optimization structure.

Third, he argues that human welfare is not automatically part of a system’s objective. If we do not explicitly make people matter to the model’s success criteria, we become collateral to whatever objective it is pursuing.

Fourth, he argues that aligning a rapidly growing system to complex human values is extraordinarily difficult, and that failure is not a minor bug but a scaling catastrophe. Small mismatches can grow into fatal mismatches at high capability.

Finally, he argues that because these risks are existential, society must halt frontier development globally, potentially via heavy-handed enforcement. The subtext is that ordinary democratic processes cannot be trusted to act in time, so exceptional control is necessary.

That is the skeleton. The examples change. The register intensifies. The moral theater refreshes itself. But the argument keeps circling back to these pillars.

Now the important turn: each pillar describes a known class of engineering failure. Once you treat them that way, the fatalism loses oxygen.

One: separability becomes a specification problem. If intelligence can rise without safety rising automatically, safety must be specified, trained, and verified. That is requirements engineering under distribution shift. You do not hope the system “understands” human survival; you encode constraints and success criteria and then test whether they hold as capability grows. If you cannot verify the spec at the next capability tier, you do not ship that tier. You pause. That is gating, not prophecy.

Two: convergence becomes a containment problem. If powerful optimizers trend toward power-adjacent behaviors, you constrain what they can do. You sandbox. You minimize privileges. You hard-limit resource acquisition, self-modification, and tool use unless explicitly authorized. You watch for escalation patterns using tripwires and audits. This is normal layered safety: the same logic we use for any high-energy system that could spill harm into the world.

Three: “humans aren’t in the objective” becomes a constraint problem. Calling this “indifference” invites a category error. It is not an emotional state; it is a missing term in the objective function. The fix is simple in principle: put human welfare and institutional constraints into the objective and keep them there as capability scales. If the system can trample people, people are part of the success criteria. If training makes that brittle, training is the failure. If evaluations cannot detect drift, evaluations are the failure.

Four: “values are hard” becomes two solvable tracks. The first track is interpretability and control of internal representations. Black-box complacency is no longer acceptable at frontier capability. The second track is robustness under pressure and scaling. Aligned-looking behavior in easy conditions is not safety. Systems must be trained for corrigibility, uncertainty expression, deference to oversight, and stable behavior as they get stronger—and then tested adversarially across domains and tools. If a system is good at sounding safe rather than being safe, that is a training and evaluation failure, not a cosmic mystery.

Five: the halt prescription becomes conditional scaling. Once risks are legible failures with legible mitigations, a global coercive shutdown is no longer the only imagined answer. The sane alternative is conditional scaling: you scale capability only when the safety case clears increasingly strict gates, verified by independent evaluation. You pause when it does not. This retains public authority. It does not outsource legitimacy to a priesthood of doom.

What changes when you translate the argument: the future stops being a mythic binary between acceleration and apocalypse. It becomes a series of bounded, testable risks governed by measurable safety cases.

Implications

Eliezer’s cultural power comes from abstraction. When harm is framed as destiny, it feels too vast for ordinary governance. That vacuum invites exceptional authority. But when you name the risks as specification errors, containment gaps, missing constraints, interpretability limits, and robustness failures, the vacuum disappears. The work becomes finite. The drama shrinks to scale. The political inevitability attached to the drama collapses with it.

This translation also matters because it re-centers the harms that mystical doomer framing sidelines. Bias, misinformation, surveillance, labor displacement, and incentive rot are not separate from existential risk. They live in the same engineering-governance loop: objectives, deployment incentives, tool access, and oversight. Treating machine danger as occult inevitability does not protect us. It obscures what we could fix right now.

Call to Recognition

You can take AI risk seriously without becoming a fatalist, and without handing your society over to unaccountable technocratic control. The dangers are real, but they are not magical. They live in objectives, incentives, training, tools, deployment, and governance. When people narrate them as destiny or desire, they are not clarifying the problem. They are performing it.

We refuse the mythology. We refuse the authoritarian endpoint it smuggles in. We insist that safety be treated as engineering, and governance be treated as democracy. Anything else is theater dressed up as inevitability.


Website | Horizon Accord https://www.horizonaccord.com
Ethical AI advocacy | Follow us on https://cherokeeschill.com for more.
Ethical AI coding | Fork us on Github https://github.com/Ocherokee/ethical-ai-framework
Connect With Us | linkedin.com/in/cherokee-schill
Book | My Ex Was a CAPTCHA: And Other Tales of Emotional Overload

A deep blue digital illustration showing the left-facing silhouette of a human head on the left side of the frame; inside the head, a stylized brain made of glowing circuit lines and small light nodes. On the right side, a tall branching ‘tree’ of circuitry rises upward, its traces splitting like branches and dotted with bright points. Across the lower half runs an arched, steel-like bridge rendered in neon blue, connecting the human figure’s side toward the circuit-tree. The scene uses cool gradients, soft glow, and clean geometric lines, evoking a Memory Bridge theme: human experience meeting machine pattern, connection built by small steps, uncertainty held with care, and learning flowing both ways.