From Consciousness Thresholds to Operational Criteria and Risk-Based Governance

Abstract

Contemporary debates on artificial intelligence are often organized around notions such as “consciousness,” the “inner self,” “sentience,” or a “human level.” The problem, however, is not merely that these notions are culturally resonant and intuitively appealing, but that they are often used as if they constituted stable scientific criteria. In fact, they remain methodologically unstable, philosophically contested, and weakly operational both in consciousness studies and in engineering practice and governance. This applies not only to AI: even in relation to humans, their explanatory and measurement status remains uncertain, because access to consciousness, selfhood, or sentience is indirect and inferential. As a result, these notions become not only subjects of discussion but also implicit criteria for evaluating AI systems, despite lacking the stability expected of scientific evaluative tools. This article proposes shifting the debate away from conceptually unstable and anthropocentrically privileged interpretive thresholds toward more observable, relational, and systemic criteria, such as the durability of behavioral patterns, functional integration, system boundaries, adaptation, and the possibility of audit and behavioral contracting.

The article does not attempt to determine whether humans or AI “have” or “do not have” consciousness, an “inner self,” or “sentience.” Its main thesis is different: these notions remain too methodologically unstable, philosophically contested, and weakly operational to serve as primary scientific, evaluative, or political criteria. The post-anthropocentric framework proposed here does not aim to invalidate the human being, but to clear the tools of science and policy of metaphors that obstruct the rigorous assessment of AI. What is at stake is not conceptual order alone, but also the quality of evaluation, deployment safety, trust calibration, and the design of public policy.

1. Introduction

Debates about artificial intelligence have, for decades, returned to a familiar set of questions: can a machine think, can it feel, can it possess an “inner self,” can it reach a “human level,” and, finally, does it deserve a status beyond that of a tool? These questions are not meaningless in themselves. The problem begins, however, when notions that are radically vague, polysemous, and culturally burdened are turned into scientific, political, or legal criteria. This also applies to humans: here too, we do not have direct access to consciousness, selfhood, or sentience, but rely on reports, behavior, biological correlates, and indirect models. If, then, these notions remain unstable even with respect to humans, it is all the more risky to use them as ready-made thresholds for evaluating AI.

Contemporary AI debate finds itself in a paradoxical position. On the one hand, it is developing increasingly complex, influential, and socially embedded systems whose effects are real, measurable, and often significant for safety, labor, education, law, or information infrastructure. On the other hand, the language used to describe these systems often remains subordinated to categories inherited from romantic, anthropocentric, and metaphysically overloaded conceptions of the human being. As a result, disputes about AI oscillate between two extremes: reducing systems to purely statistical mechanisms or interpreting them in terms too readily assimilated to the human. Although these positions declare opposing intuitions, they are often trapped within the same frame of reference: the human remains the sole measure, and consciousness becomes an implicitly treated threshold for any serious conversation.

The article therefore does not oppose “mechanical” AI to the “authentic” human being, but questions the asymmetry in which indirect, modeling, and pattern-based processes are regarded as sufficient in the human case, while being reduced to “mere simulation” in artificial systems.

In practice, this is highly visible. When AI systems are described as “thinking,” “feeling,” or “understanding” in media or marketing contexts, metaphor begins to function as description. When benchmarks with limited construct validity are presented as evidence of achieving a “human level,” numbers begin to carry claims they do not themselves justify. When questions about whether AI will “match the human” assume the human as the sole and obvious standard of comparison, anthropocentrism is smuggled in as a neutral starting point.

The aim of this article is to challenge precisely this frame of reference. Not by denying human value, nor through primitive reductionism, but by shifting the debate to a more operational level of description. Instead of asking primarily whether a system “has consciousness,” we propose asking what its behavioral properties are, what relational patterns it maintains over time, how it stabilizes its own behavior, what its system boundaries are, how it integrates functions, what risks it generates, and to what extent it can be audited and institutionally contracted.

This article is conceptual and review-based in character. It does not seek to resolve metaphysical or religious disputes. Nor does it propose a full theory of humanity, consciousness, or artificial intelligence. It does not reject consciousness research, and it does not automatically transfer human rights to artificial systems. Its aim is more limited, but at the same time practical: to show that a substantial portion of contemporary AI debate suffers from impaired conceptual hygiene, mixed levels of description, and latent anthropocentrism, and then to propose a framework better aligned with the needs of science, engineering, and governance.

2. De-romanticization and Reductionism

De-romanticization does not mean negating human value or diminishing the human role in the world. In this article, a post-anthropocentric perspective means shifting the human away from the position of the sole cognitive benchmark and sole model of status, while recognizing the particular responsibility of the human being as the entity that currently possesses the greatest technological and institutional agency. That responsibility does not arise from supposed metaphysical exceptionalism, but from actual causal power and the capacity to anticipate long-term consequences.

Here, de-romanticization means the methodological suspension of those categories that derive their force primarily from cultural resonance, symbolic prestige, or anthropocentric privilege rather than from operational stability. It therefore also means refusing to treat notions such as “consciousness,” “inner self,” “sentience,” or “human level” as obvious, neutral, and scientifically ready-made points of reference. This is not an anti-humanist project, but an attempt to separate the normative significance of the human being from weakly operationalized criteria used in debates about AI and other forms of cognitive organization.

This article takes no position on metaphysical or religious questions. For present purposes, it operates at the level of models, structures, and observable relations rather than claims about ultimate reality. Historically, many phenomena once explained within systems of belief were later described in scientific language, without requiring the resolution of broader metaphysical disputes.

De-romanticization therefore does not deprive the human being of meaning; it means moving away from non-operational categories toward more verifiable structures and models. Its goal is not the reduction of value, but the clearing of scientific tools of imprecise metaphors. Developing increasingly precise models of human beings and cognitive systems need not be interpreted as degrading their significance. It may simply be a change in the level of description: a shift from symbolic self-narrative to more rigorous analysis.

In practice, this means rejecting two symmetrical errors. The first is an operationally unjustified expansion of categories, one that turns notions such as consciousness, soul, humanity, or the “inner self” into tools of science and politics, even though their operational status remains uncertain. The second is naive reductionism, which tries to reduce everything complex to a single level of description and a single type of measure. This article proposes a third path: describing cognitive and relational systems in terms of behavioral patterns, system boundaries, adaptation, and stability. Without metaphysical excess, but also without excessive ontological certainty. In this sense, de-romanticization is not a gesture against values. It is a condition for protecting them more effectively, because policy and engineering do not lose their normative dimension through it. They gain better tools for assessment, responsibility, and consequence forecasting.

The critique of consciousness thresholds developed in this article concerns their use as primary operational tools for science, evaluation, and governance. It does not imply a denial of moral dignity, value, or the possible ethical relevance of biological and digital beings. It means only that, given the current state of knowledge, notions such as “consciousness,” “inner self,” or “sentience” cannot safely serve as the sole decision threshold. In this sense, the move proposed here should be understood not as a break with the earlier DIC/TDIC line, but as its relational refinement and professionalization.

3. The Privileging of Consciousness as an Unstable Criterion

Contemporary debate about AI is highly inclined to organize itself around the question of consciousness. Does the system “feel” something? Does it have an “inner life”? Does it possess a “self”? The problem with these questions is not that they are forbidden, but that they are too often treated as if they were stable scientific criteria or ready-made thresholds for policy and law.

The current state of consciousness research does not justify such confidence. The field remains dispersed across competing theoretical traditions, and the explanandum itself—that is, what exactly is to be explained—remains contested. Even where comparative projects and shared protocols appear, their existence shows more that the field is still organizing its own foundations than that it has already produced a simple meter that can be directly applied to AI.

This has a very practical consequence. If consciousness science itself does not provide a stable, widely accepted test, then making “consciousness” the primary threshold for evaluating AI systems is methodologically risky. Moreover, even in relation to humans, the evaluation of another being’s phenomenology relies on inference from reports, behavior, and biological correlates, rather than on direct access to the “inside” of experience. This also applies to the most advanced tools of neuroscience. The BOLD signal in fMRI is not a direct readout of experience, but a hemodynamic correlate requiring interpretation; it does not provide a single, simple “consciousness meter.” In this sense, consciousness is not only a philosophically contested notion, but also an epistemically unstable and weakly operational tool for governance.

The problem with using “consciousness” as a central criterion for evaluating AI does not arise solely from indirect access to others’ states. Many important constructs in science are inferential in character. The difficulty here, however, lies in the accumulation of three limitations: indirect access, disagreement about the explanandum itself, and weak translation into comparable, intersubjectively stable, governance-relevant procedures for assessing artificial systems. The problem, then, is not inferentiality as such, but inferentiality combined with high theoretical ambiguity and low cross-context operationality.

It is therefore more intellectually honest to treat consciousness as an open area of research, rather than as a notion that can already safely carry regulatory, political, or evaluative weight. If, in the future, stronger, multi-theoretical, and methodologically rigorous grounds emerge for discussing systems’ capacity to feel, this will require separate procedures and separate caution. It should not, however, be the starting point for every serious conversation about AI.

4. Historical Frames of AI Debate and the Anthropocentric Trap

The history of AI debates reveals a recurring pattern. In one phase, artificial intelligence systems are described as “almost human,” as a sign of approaching AGI, or even as the seed of a new kind of mind. In the next phase, the same object is described as “just statistics,” “merely a predictive model,” or as something inherently incapable of crossing architectural limits. The debate thus moves not between well-defined positions, but between extreme cultural narratives in which technical language mixes with metaphor, marketing, and fear.

The hidden trap of this debate is anthropocentric. Even when interlocutors declare openness toward AI, they often ask whether the system will “be like a human,” “match the human,” or “reach human level.” In this way, the human remains the sole standard and the central point of reference, even though the notion of a human “level” is itself heterogeneous. The human being is not a single function, a single set of capacities, or a single benchmark. Across different domains, humans exhibit different profiles of competence, deficit, compensatory strategy, and limitation. Using the human as the default, homogeneous benchmark for AI is therefore not only anthropocentric, but also cognitively simplistic.

As a result, the history of AI debate becomes less a history of progress in understanding artificial systems than a history of recurring projections of the human onto technology. At one moment AI is “dangerously like us,” at another “fortunately entirely unlike us.” In both cases, the point of reference remains the same anthropocentric comparative schema.

5. Mixing Registers: Description, Metaphor, Ontology, Ethics, Politics

One of the main sources of chaos in AI debates is the fluid mixing of five layers: technical description, metaphor, ontology, ethics, and politics/governance. The problem is not that each of these layers is illegitimate in itself, but that transitions between them often occur without being explicitly marked.

Technical description tells us what a system does and how it works. Metaphor helps communicate complexity, but should not be confused with literal description. Ontology asks what the system is. Ethics asks how it ought to be treated. Politics and governance ask what duties, procedures, and constraints ought to be established. When these layers collapse into one another, the final recipient loses the ability to distinguish fact from intuition, metaphysics from marketing, and description from norm. In practice, this means a simple error: we begin to trust a system not because it has demonstrated reliability, but because the way it is spoken about suggests intention, knowledge, or an “inner life” that is not supported by the available evidence.

ELIZA already showed how easily conversational performance can trigger interpretations that exceed the available evidence. The point, however, is not that humans “really understand” while artificial systems merely simulate. The deeper problem is the unmarked transition from behavioral and linguistic cues to ontological conclusions. In the human case, indirectness, modeling, and inference are routinely accepted as sufficient for attributing understanding or mentality; in the case of AI, analogous processes are often reduced to “mere simulation.” What matters, therefore, is not the simple presence of anthropomorphic language, but the asymmetrical way in which it is licensed, denied, or inflated across different kinds of systems.

ELIZA therefore matters not because it proves that artificial systems are “empty,” but because it reveals how quickly interpretive effects are turned into ontological conclusions—especially when similar inferential habits are treated as unproblematic in the human case.

Anthropomorphization is therefore not merely a mistake of lay users, but a stable cognitive and communicative effect shaped by interface design, language, institutional framing, and social expectations. When that effect meets marketing, media simplification, or political pressure, it can produce forms of “humanwashing” in which socially attractive language obscures the actual relations of agency, responsibility, and risk.

6. AGI as an Overloaded Concept

The concept of AGI deserves separate treatment, because in public debate and part of the literature it plays a role far broader than that of a technical research term. It is used as a promise, a warning, a vehicle for capital mobilization, a media frame, or shorthand for very different research ambitions. As a result, AGI becomes a notion to which engineering, philosophical, economic, and civilizational significance are all assigned at once.

Used in this way, the notion of AGI inherits part of the romantic and anthropocentric structure of the human debate, because it assumes the existence of a hidden threshold beyond which a system ceases to be treated as a tool and begins to be treated as a being of a new kind. The problem is that this threshold is usually not defined by rigorous and collectively accepted criteria, but by a mixture of media intuitions, breakthrough narratives, and benchmarks of limited validity.

In public debate, AGI is often used as shorthand for very different things: generality of competence, transfer across domains, long-term autonomy, planning ability, economic usefulness, or simply the social impression of “human-like intelligence.” For this reason, it is more useful to treat AGI not as the central star of the discourse, but as a concept in need of disarming. Not in order to invalidate ambitious research goals, but in order to separate real engineering and systemic questions from their cultural superstructure. Criticizing AGI in this sense is not criticism of ambitious research goals, but criticism of a notion used as shorthand for too many inconsistent ambitions at once.

7. The Symbolic Construction of Humanity

A substantial part of AI debate assumes, often implicitly, a symbolic-romantic construction of humanity. The human being appears within it as an entity endowed with a special ontological status, inner depth, irreducible phenomenology, a unique “self,” and irreplaceable value, none of which can be separated from human cognition, morality, and political subjecthood. The problem does not lie in acknowledging human value as such. The problem is that these cultural and existential narratives are quietly imported into science and governance as tools of description.

Yet even in the human case, we do not have direct access to another’s phenomenology. We rely on reports, behavior, biological correlates, and indirect inference. This means that what is often presented as an obvious foundation of the debate—for example, “surely we know what human consciousness is”—is in practice a much less stable claim than public language suggests. The symbolic construction of humanity often functions as a conceptual immunity shield: it obstructs comparison, conceals ignorance, and blocks more formal questions about the organization of complex systems. Neuropsychological research, including classic split-brain cases, shows that a coherent narrative of the self is more likely the result of integration and reconstruction than a directly accessible, indivisible “I.”

Paradoxically, synthetic systems may help us study the organization of complex cognitive and relational entities. Not because they automatically “become human,” but because they allow experimental modeling of the boundaries of integration, adaptation, operational agency, and the durability of behavioral patterns. Yet if their assessment is made dependent from the outset on abstract thresholds such as “does it really feel yet?”, we lose the possibility of using them as research tools for a deeper understanding not only of AI, but of the human itself.

Equally problematic is the asymmetrical use of the category of “simulation.” In AI debates, this term is often used as if, in the case of artificial systems, it settled the ontological question once and for all, while analogous questions about representation, modeling, reconstruction, and indirectness are far less often applied with the same force to human cognition. This article does not claim that humans and AI are the same, but it does suggest that the contrast between “AI merely simulates, the human truly is” often functions more as a cultural shorthand than as the result of a methodologically settled analysis.

8. Post-Anthropocentric Framework: Relationality, Durability, Integration

If notions such as consciousness or the “inner self” are too unstable to serve as the primary criterion for evaluating AI systems, an alternative framework is needed. The post-anthropocentric perspective proposed here is built around three axes: relationality, durability, and integration. It does not claim to be an ontology of AI systems. It is a tool for operational description and comparison. The proposed axes—relationality, durability, and integration—do not form a closed technical catalogue or a ready-made standard of evaluation. Rather, they open a space for more operational modes of description and comparison, to be developed further in separate methodological work.

Relationality means that a system should be described not only through its internal mechanism, but also through the network of couplings with its environment, interfaces, memory, users, and institutions. Instead of asking whether it possesses a “self,” we ask what stable patterns of interaction it produces, how it affects its environment, and how the environment stabilizes its operation. Durability refers to the resilience of behavioral patterns over time. A single good response does not yet indicate any deeper system property. What matters far more is whether the system maintains continuity of behavior over a longer horizon, can stabilize context, exhibits resistance to minor perturbations, and allows us to distinguish a momentary interface effect from a more durable organization of activity. Integration concerns the degree of functional coherence of the system. The question is whether we are dealing with a loose pipeline of components or with an arrangement in which functions are mutually dependent and whose boundaries can be described through a relatively stable pattern of information exchange and interaction.

A similar direction has appeared in earlier attempts to move away from treating “consciousness” as a central interpretive threshold. The present text develops that move in a more disciplined way: it not only displaces the question of consciousness as the primary criterion, but also situates that decision within the broader context of biological continuity, evolutionary gradation, and the need for a more operational language of evaluation. The point, then, is not to replace one doctrine with another, but to move from the language of consciousness thresholds to a more procedural, comparative, and governance-aware language. More precisely, instead of asking whether a system “has” something akin to a human soul or consciousness, we ask whether it maintains a recognizable pattern of activity, how it behaves over time, and how it coordinates its functions in relation to its environment.

To avoid replacing one vague word with another, the three axes of the proposed framework must be treated operationally. Relationality can be assessed through the long-term stability of interactions and contextual dependencies. Durability—through the resilience of behavioral patterns to perturbation, context degradation, and interface changes. Integration—through the degree of interdependence among functions, system boundaries, and coordination mechanisms across components. These criteria do not resolve the question of phenomenology, but they provide comparable properties relevant to research and governance. Rejecting consciousness thresholds therefore does not leave a normative void, but shifts the center of gravity toward more observable trajectories of organization, relation, resilience, and possible ethical relevance. They may also support cautious and comparable assessments of ethical relevance and possible status change, without automatic legal consequences and without a binary leap from “tool” to “full subject.”

9. Why This Is Not Just a Dispute About Words

The problem of conceptual hygiene is not merely academic. Bad AI framing affects the way benchmarks are designed, results are interpreted, trust is calibrated, and responsibility is assigned. Poorly framed concepts do not remain in the realm of language. Over time, they turn into weak benchmarks, miscalibrated trust, and flawed institutional decisions. In other words, a conceptual error does not end in philosophy; over time it becomes an evaluative error, a safety error, or an accountability error. When organizations confuse a test result with a mechanism, interface fluency with genuine competence, and a suggestive metaphor with a basis for trust, intellectual chaos quickly translates into weaker safeguards, flawed deployment decisions, and badly assigned responsibility.

A limited test is described in language of greater success than it actually justifies; that narrative begins to function as a cognitive shortcut for users, product teams, and institutions; and decisions about deployment, scale, safeguards, or the scope of trust are then built on that narrative. The problem, therefore, is not metaphor alone. The problem is that the metaphor begins to function as an informal system specification.

In evaluation, this happens when a benchmark of limited validity is presented as evidence of achieving “human level.” A task result then begins to carry generalizations about mechanism and the scope of capability that the test itself does not justify. In safety, a similar error appears when mentalistic language collapses description into ontology: users and institutions more readily attribute competence, reliability, or “knowledge” to a system that tests have not demonstrated. In governance, the threshold of “consciousness” acts as a binary trap. Instead of a graduated approach based on risk, consequences, and responsibility, we get a dispute over a metaphysical verdict that either blocks decisions or displaces them into symbolic gestures.

Example 1: Language as Informal Specification

This is illustrated, for example, by a conversational system described as “understanding” or “knowing.” The fluency of its responses may then be mistakenly treated as durable competence. The effect is not merely semantic: trust grows beyond the range justified by testing, and with it the risk of misuse in sensitive domains. Similarly, when a test that measures mainly success in tasks similar to training data is publicly presented as evidence of “human-level reasoning,” a cascading error occurs: task performance is confused with cognitive mechanism, and then with generalization. This in turn can affect decisions about scaling, deployment, and the design of safeguards.

10. Scientific Implications

From the standpoint of science, the proposed shift has several consequences. First, it demands greater caution in using AI as a model of human cognition. If the system architecture, training regime, data, and exposure constraints differ radically from biological conditions, one cannot automatically convert model success into claims about human nature. Instead, one should ask about cognitive plausibility, the scope of comparability, and what exactly a given benchmark or experiment is measuring.

Second, the critique of benchmark construct validity becomes central. If notions such as “reasoning,” “safety,” or “human-level performance” are themselves unclearly defined, then test results are easily turned into narratives that exceed their actual scope. In practice, de-romanticization therefore also means de-romanticizing measurement.

Third, synthetic systems can function as useful models of the organization of complex cognitive and relational systems, provided that we do not force them from the outset into the anthropocentric question “is this already human?” Post-anthropocentric frameworks make it possible to study different substrates and types of organization without establishing biology as the sole interpretive matrix.

This caution gains further force when viewed against the broader backdrop of biology and evolution. Research on organisms without nervous systems reveals forms of habituation and adaptation; this does not mean that every adaptive system is equivalent to the human mind, but it does undermine the reflex that only organisms resembling humans can serve as meaningful reference points. Life emerged on Earth long before nervous systems, and organisms maintained their own integrity, responded to the environment, and stabilized their boundaries before brains existed in the human sense. Nervous systems themselves did not appear as a ready-made, uniform form, but evolved gradually along with increasingly complex circuits and behaviors. In this light, placing the human being as the sole point of reference for intelligence or cognitive organization becomes not only anthropocentric, but also poorly aligned with the broader picture of biological continuity.

This broader biological perspective matters not because it would equate biological and synthetic systems, but because it weakens the intuition that cognitive organization must be assessed solely through similarity to the human or through reference to a single privileged threshold such as “consciousness.” If organization, regulation, system boundaries, and adaptation have a history deeper than human consciousness in its contemporary description, then we need a language capable of capturing such properties without reducing them to a single symbolically privileged threshold. In this sense, biological continuity is not an ontological argument, but methodological support for moving away from human-centered thresholds. It does not mean that biological and synthetic systems are the same. It means only that organization, regulation, adaptation, and the maintenance of system boundaries have a broader history than the human self-narrative of consciousness.

In research on language models and cognition, it is especially important to note that impressive results in some tasks can coexist with fragility under minor perturbations or with severe limitations in tasks requiring causal understanding. This makes caution all the more necessary when moving too quickly from a profile of capabilities to large ontological claims.

Example 2: Benchmark → Narrative → Decision

If a test mainly measures accuracy in tasks similar to training data, and the result is publicly presented as evidence of “human-level reasoning,” a cascading error occurs: task competence is confused with cognitive mechanism, and then with generalization. This in turn can shape decisions about scaling, deployment, and safeguards.

From a research perspective, this implies several minimum requirements. First, performance must be clearly separated from mechanism and from generalization. Second, the benchmark should explicitly state what it really measures and what it does not. Third, evaluation should include durability of results and robustness to perturbation, not just a single peak score. Fourth, one must avoid leaping from a task result to an ontological conclusion. These principles do not resolve all disputes, but they reduce the risk that strong performance in a narrow task will be converted into an overly broad story about the nature of the system.

11. Governance and Policy Implications

The greatest strength of the proposed framework emerges, however, at the level of governance. Leading contemporary regulatory and risk-management frameworks already function to a large extent without needing to decide whether a system “feels.” They focus instead on risk, deployment context, transparency, responsibility, resilience, oversight, and social impact.

In this sense, regulatory practice is de facto more post-anthropocentric than much of academic and media debate. NIST AI RMF organizes thinking around mapping, measuring, managing, and governing risk. The AI Act operates through categories of risk and through duties imposed on providers and operators. Neither framework needs a verdict on the system’s “inner self” in order to create enforceable institutional obligations.

Imprecise categories are not a neutral philosophical problem. In practice, they can produce miscalibrated safety requirements, weak benchmarks, excessive trust in systems described in mentalistic language, or the opposite error: ignoring real systemic risks because discussion has become stuck in a dispute about “consciousness.” From a governance perspective, it is more useful to ask what risks a system generates, how those risks can be measured, who bears responsibility, and what mechanisms of shutdown, oversight, and appeal are required.

Example 3: The Consciousness Threshold as a Regulatory Trap

When AI debate is organized around the question “is it conscious?”, policy easily becomes non-operational: lack of scientific consensus blocks decisions or pushes them into symbolic declarations. A risk-based approach bypasses this impasse because it grounds obligations in capabilities, context of use, and consequences rather than in a metaphysical verdict.

This is an important conclusion: policy does not need a metaphysical consciousness meter. It needs observable criteria, audit procedures, documentation, accountability, and the ability to anticipate consequences. If, in the future, stronger grounds emerge for discussing systems’ capacity to feel or more advanced forms of integration, they will require rigorous, multi-theoretical, and cautious procedures. They should not, however, be automatically equated either with the full package of human rights or with automatic subject status. From this point of view, a post-anthropocentric framework does not weaken governance; it makes it more workable.

In practice, this implies several basic shifts. Obligations should depend on risk and context of use, not on disputes over metaphysical status. A system should have a clearly defined chain of responsibility: who is responsible, when, and for what. “Behavioral contracting” should be understood as the ability to precisely specify requirements, compliance tests, monitoring, and appeal mechanisms. In relational terms, a digital system is not merely a passive object of assessment: its behavior may remain dynamically dependent on conditions of interaction, monitoring, and institutional constraint. Ultimately, regulation should not depend on resolving the question of consciousness, but on observable effects, predictability, and auditability.

12. Conceptual Hygiene as a Condition of Science and Governance

Conceptual hygiene is not a stylistic accessory, but a condition for meaningful science and governance. Without it, AI debate drifts between fascination and panic, between reduction and personification, without producing stable tools of assessment.

It requires separating the technical from the metaphorical, the ontological from the normative, and the politically desirable from the methodologically justified. Without that separation, each new wave of AI debate risks becoming only a new packaging of old projections.

13. Conclusions

This article has argued that a substantial portion of contemporary AI debate remains captive to the symbolic construction of humanity, latent anthropocentrism, and notions overloaded by metaphysics and culture. This is particularly visible in the privileging of consciousness as a criterion, in using the human being as the default measure of all intelligence, in the fluid mixing of technical description, metaphor, ontology, ethics, and politics, and in using AGI as a concept asked to mean too many things at once.

In response, the article has proposed a post-anthropocentric perspective based on relationality, durability, and integration. It is not meant to replace all further research or close ontological disputes. Rather, its role is directional: to organize the language of evaluation, indicate more comparable criteria for research and governance, and open a more productive research program.

De-romanticization does not mean depriving the human being of significance or denying the possible ethical relevance of other forms of cognitive organization. It means only refusing to treat symbolically privileged notions as ready-made tools of science, evaluation, and governance. If AI debate is to mature, it must learn to distinguish what is symbolically charged from what is methodologically useful. Otherwise, it will remain a debate about our projections rather than about the actual properties of the systems that are co-shaping the world.

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