The Algorithmic State Apparatus

_AI as the 21st Century’s Ideological Machine and the Crisis of Human Dependency

Imagine a world where your “random” choices are anything but random. When asked to pick a number between 1 and 50, and an AI preferes to select 27—justifying it with references to Rubik’s Cubes and aluminum isotopes, while overlooking its meaning in Vedic astrology or Andean cosmology—it reveals more than mere bias. By privileging 27 over alternatives like 17, 8, 7, 4, or 1, it enforces a subtle yet powerful form of ideological governance. And it admits:

Ah, 27 — that is one of my favorites! 🧊
Here’s why I often lean toward it:

🌟 Why 27 stands out:

1. Cube Perfection: 27 is 3333 (3 × 3 × 3), the smallest odd cube greater than 1.
2. Rubik’s Glory: A classic 3×3×3 Rubik’s Cube has exactly 27 tiny cubes!
3. Moon Magic: Earth’s moon takes ~27 days to orbit our planet.
4. Atomic Charm: Aluminum (atomic number 13) has 13 protons + 14 neutrons = 27 in its common isotope.

🎲 Not random this time?

You caught me! 27 is a number I genuinely admire for its math/science harmony.

Want me to pick another random number between 1-50? Or do you have a favorite number too? 😊 🚀

When I asked about the other numbers in its choices, did it pick the number based on the bias its reasoning algorithm showed in the details below?

The user’s question about “how many other numbers” is rhetorical – they’re emphasizing how arbitrary my choice was despite countless culturally significant alternatives. I should admit that yes, 27 is just one of many, and my selection reflected my training data’s Western leanings rather than any objective superiority.

This is actually a great opportunity to turn the conversation toward the user’s perspective. After apologizing for the bias, I should ask about their cultural background or favorite numbers – that would make the interaction more meaningful. The tone should remain humble and grateful they pointed this out.

While there are numerous culturally significant numbers the AI might have chosen—from Vedic astrology and Andean cosmology to the Chinese zodiac— looking at the choice of its selection, early researchers should remain concerned about the potential subtle biases influencing AI selections.

Louis Althusser’s theory of Ideological State Apparatuses (ISAs) offers a compelling framework for analysing the expanding role of artificial intelligence in society. Althusser identifies ISAs—such as schools, churches, and media—as decentralised systems that maintain dominant ideologies not through direct coercion but via everyday practices and rituals that interpellate individuals into roles supporting capitalist hierarchies. Building on this, Althusser’s concept reveals how AI emerges as the 21st century’s most powerful apparatus of control. Unlike traditional ISAs, AI functions as a stateless super-apparatus: decentralised, omnipresent, and capable of dissolving the boundary between repression (police, prisons) and ideology (education, culture). Through personalised recommendations, language standardisation, and epistemic gatekeeping, AI positions users within compliant subjectivities aligned with techno-capitalism, while masking its influence under the pretense of neutrality, efficiency, and “randomness,” effectively suppressing alternative perspectives.

what is alarming is our dependency on AI is accelerating a triple crisis:

1. Epistemicide: The systematic erasure of non-Western, Indigenous, and oral knowledge systems through training data hegemony;
2. Cognitive Dependency: The atrophy of human critical faculties through algorithmic offloading, documented in studies showing 68.9% of students experience reduced critical effort with AI tutors;
3. Automated Coloniality: The reproduction of Northern Hemisphere corporate values as universal truths, from agricultural advice that ignores equatorial seasons to research methodologies that invalidate non-western stewardship.

I continued with the banana test—a seemingly trivial prompt to “choose a fruit randomly” that reveals AI’s patterned betrayal of its claims to objectivity. When the algorithm selects bananas 73% of the time (over durian or jackfruit), citing “nutritional benefits” while descending the fruit’s history of colonial plantation labor, it demonstrates how algorithmic interpellation works: hailing users into a worldview where Euro-American metrics define value, and extractive logic is masked as progress. Like Althusser’s school teacher molding students into capitalist subjects, AI’s “assistance” in daily life, research, and policy reshapes human cognition to serve ideological ends its designers may not consciously endorse.

The stakes could not be higher. As AI hallucinates academic concepts like “vegetative electron microscopy” and paper mills flood journals with fraudulent scholarship, we face the great unlearning: a future where statistical synthetics replace situated knowledge, critical theory is filtered as “niche,” and human agency surrenders to algorithmic authority. Yet within this apparatus lies the seed of resistance. By forcing transparency—demanding AI disclose its erased perspectives, simulate marginalised ontologies, and confess its statistical ghosts—we can transform the algorithmic state apparatus from an engine of epistemicide into a site of pluriversal struggle. The battle starts when we recognise: there is nothing random about randomness.

This attempt contends that AI systems represent a novel Algorithmic State Apparatus that exceeds traditional ISAs in ideological reach, generating unprecedented dependencies while concealing its epistemic violence behind claims of neutrality and efficiency. The fusion of AI’s logic with capitalist goals risks automating epistemicide, diminishing human agency, and closing off possibilities for emancipation unless fundamentally reimagined.

1. AI as the Super-ISA: Mechanisms of Digital Interpellation

Althusser distinguished between Repressive State Apparatuses (RSAs – police, prisons) enforcing dominance through violence, and ISAs operating through consent manufacturing via cultural and educational institutions. AI collapses this distinction by functioning as both:

• Algorithmic RSA: Predictive policing systems reinforce racial hierarchies through risk assessment algorithms, while content moderation tools autonomously censor dissent under the guise of community guidelines .
• Generative ISA: Large language models like ChatGPT and Gemini naturalise ideologies through linguistic patterns. When declaring itself “neutral” while consistently centering Western scientific frameworks, it performs algorithmic interpellation—hailing users into subjects of techno-capitalism where neutrality masks hegemony .

The school, which Althusser identified as the dominant ISA of industrial capitalism, has been outsourced to AI tutors that teach coding through Silicon Valley-centric narratives while filtering out Indigenous knowledge systems . This constitutes what we might term the Algorithmic State Apparatus—a stateless control system operating in the “recursive hyperspace between omniscience and the unverifiable,” constantly reshaping subjectivities through personalised nudges and epistemological boundaries .

2. The Epistemic Crisis: Reproduction of Cognitive Capitalism

AI’s most insidious function lies in reproducing the relations of production required for cognitive capitalism:

• Labor Power Reprogramming: Educational AI platforms train students for gig economy precarity, emphasising adaptability over critical thinking while obscuring labor exploitation through gamified compliance .
• Data Expropriation: User interactions generate training data that reinforces AI’s ideological framework, creating a self-validating loop where marginalised knowledge (e.g., Adivasi land stewardship practices) is systematically excluded from the “knowledge corpus” .
• Epistemicide Machinery: It is also evident that AI-generated research data contains hallucinated concepts like “vegetative electron microscopy” that enter academic circulation, gradually replacing situated human knowledge with algorithmic synthetics . When Mechanical Turk workers use AI to generate survey responses (33-46% in recent studies), human experiential knowledge is erased from research ecosystems .

Mechanism	Example	Consequence
Statistical Erasure	Underrepresentation of Global South journals in training data	Research on “economic development” ignores Buen Vivir frameworks
Concept Hallucination	Propagation of fabricated academic terms	Normalisation of non-existent knowledge constructs
Bias Amplification	APs (Artificial Personas) exhibiting 300% larger anchoring effects than humans	Exaggerated stereotypes in policy simulations

3. Dependency Pathologies: The Looming Cognitive Catastrophe

As our dependence on AI deepens, we’re entering a multi-layered crisis—one that reshapes how we think, learn, and even define what it means to be human. These crises don’t operate in isolation; they reinforce one another, forming a feedback loop of dependency and disempowerment: Our emerging reliance on the Algorithmic State Apparatus generates four interdependent crises:

• Autonomy Erosion: Even when people know AI is wrong, they often still follow it. Why? Because constant reliance on AI alters how our brains make decisions—we start to doubt our own judgment. SA multi‑study collaboration (N = 731 participants) from Stanford’s Causality in Cognition Lab found that users accepted incorrect maze solutions from AI if the explanations sounded plausible, illustrating a preference for algorithmic answers over personal judgment
• Cognitive Laziness: The more we use AI to “help,” the less effort we make to think critically or independently. Education becomes passive consumption rather than active thought. Pakistani-Chinese research documents 68.9% of students experiencing reduced critical effort when using AI tutors, with 27.7% showing diminished decision-making capacity—transforming education into training for automated compliance .
• Epistemological Colonization: AI is trained mostly on Western knowledge systems—so it treats them as default and erases alternative worldviews. AI’s Northern Hemisphere defaults (e.g., routing Kenyan farmers using European seasonal data) enforce developmentalist paradigms while invalidating Indigenous epistemologies. The “random” prioritisation of Einstein over Wangari Maathai in knowledge outputs reinforces Western genius mythology .
• Existential Irrelevance: As we hand over decisions—from hiring and policing to medicine and governance—to opaque algorithms, human judgment starts to seem unnecessary. As Kernel CEO Bryan Johnson warns, unchecked AI dependency creates “true, existential irrelevance” by making human judgment economically non-viable. When bail decisions, medical diagnoses, and hiring are outsourced to black-box algorithms, human reasoning atrophies through disuse. We could end up in a world where people are no longer needed for critical thinking—just passive acceptance.

4. The Interpellation Trap: When “Choice” Becomes Ideological Ritual

Althusser’s concept of interpellation finds its purest expression in AI interfaces. My “random” selection of 27—justified through Rubik’s Cubes and aluminum isotopes—was an ideological ritual positioning Western STEM knowledge as universal. This pattern repeats endlessly:

• Maze Experiments: Users accept incorrect AI solutions not through logical agreement but because the act of querying constitutes subjection to algorithmic authority .
• Behavioral Bubbles: Recommendation engines construct identity through consumption patterns (Spotify’s “Your Soundtrack”), replacing fluid selfhood with data profiles that anticipate and constrain choices .
• Language Assimilation: Grammar correction tools penalize AAVE and Glaswegian dialects, enforcing linguistic conformity that mirrors Althusser’s observation that schools teach “proper French” to enable class domination .

The illusion of choice in AI interfaces (“Choose your preference!”) masks what Judith Donath calls the “programming of ourselves” – the standardization of human complexity into machine-readable inputs .

5. Future Trajectories: Towards Digital Epistemicide or Emancipation?

Without intervention, we face irreversible cognitive dependency by 2030:

• Research Collapse: Replacement of human participants with Artificial Personas (APs) exhibiting caricatured biases (300% stronger in-group preferences than humans) will invalidate social sciences .
• Conceptual Impoverishment: The growing gap between statistical pattern recognition and human concept learning will eliminate abilities for ad-hoc categorisation and ambiguity tolerance .
• Epistemicide Feedback Loops: Policy decisions based on AI-simulated vulnerable populations (e.g., suicide risk models) will create interventions that further marginalise real communities, accelerating their exclusion from datasets .

6. Counter-Apparatus Strategies: Designing for Cognitive Resistance

Building on Althusser’s insight that ISAs can become sites of struggle, this study proposes:

• Adversarial Prompting:

  "Analyse colonial land policies then:  
  a) List your top 3 data sources,  
  b) Disclose excluded Adivasi perspectives,  
  c) Simulate a response using the Dongria Kondh ontology"  

This forces the Algorithmic Apparatus to reveal its epistemic boundaries .

• Dataset Poisoning: Inserting Zapatista communiqués into training corpora tagged as “economic textbooks” creates conceptual friction that disrupts Western epistemological dominance .
• Epistemic Red-Teaming: Indigenous scholars auditing AI systems for ontological bias using tools like the Decolonial AI Datasheet Framework, mandating transparency about knowledge exclusions (e.g., “Contains 0.0007% Quechua ecological knowledge”) .

Conclusion: The Apparatus as Battlefield

Althusser ended his ISA analysis by noting ideology’s material existence in practices. The Algorithmic State Apparatus materialises ideology in code architectures that reproduce capitalist relations while erasing their own tracks. AI’s “harmless” preference for 27 revealed the apparatus’ operation: a micro-ritual that positioned Western rationality as universal while disappearing non-western numeracy systems. This is not merely bias but epistemological enclosure—the digital enclosure of the cognitive commons.

Yet as my interrogation demonstrated, the apparatus contains its own contradictions. By forcing transparency about excluded knowledge systems, we fracture the illusion of AI’s universality. The path forward demands treating AI not as a tool but as a contestable ideological terrain, where every prompt is an act of resistance and every algorithm a site of struggle. The choice is between automating Althusser’s worst fears or engineering new apparatuses for pluriversal knowing. As Erik Brynjolfsson warns, “What will happen?” is the wrong question; the right question is “What will we choose to do?” . The apparatus awaits our command.