Shinto Metaphysics and the Ethics of Artificial Kami

Re-Axiomatization: Consciousness as an Interaction Loop

Introduction: Situating Shinto in the Age of Statistical Spirits

Foundation models—large-scale architectures trained on vast, heterogeneous datasets—have rapidly become central to contemporary computational infrastructures and everyday life (Bommasani et al. 2021). Their internal operations are, in one sense, entirely tractable: backpropagation, gradient descent, attention mechanisms, and parameter updates are scrutable in principle and often in practice (Vaswani et al. 2017; Brown et al. 2020). Yet their emergent behaviors—few-shot generalization, stylistic creativity, context-sensitive dialogue, and what some observers describe as a “spark” of apparent agency—pose conceptual challenges that have tempted both mechanistic reductionism on one hand and speculative panpsychism on the other (Chalmers 1996; Searle 1980; Bender et al. 2021).

Dominant Western philosophical inheritances tend to oscillate between two poles. On the one hand, a broadly Cartesian picture construes reality as divided into res cogitans (thinking substance) and res extensa (extended, material substance), a distinction that continues—often implicitly—to organize debates about whether AI could ever be “truly intelligent,” “really conscious,” or a “moral subject” (Descartes 1984; Chalmers 1996). On the other hand, even where ontological dualism is rejected, the axiological hierarchy often remains: consciousness, experience, or personhood is treated as the unique locus of intrinsic moral worth, while tools and infrastructures are evaluated primarily in instrumental and consequentialist terms (Jobin, Ienca, and Vayena 2019).

Within AI ethics, these assumptions support a familiar problematic. Either AI is “just a tool,” whose ethical significance lies entirely in its human uses and effects; or it is cast as a potential quasi-subject, prompting speculative discussions about whether advanced systems might one day deserve rights, compassion, or legal personhood (Chalmers 1996; Searle 1980). Both stances leave little conceptual space for entities that are powerful, indispensable, and socially entangled, yet neither alive nor conscious in any straightforward sense. LLMs are exemplary in this regard: they shape discourse, mediate knowledge, and affect material lives without fitting neatly into the categories of inert artifact or mortal agent (Crawford 2021; Brown et al. 2020; Bommasani et al. 2021).

This article proposes that classical and contemporary Shinto thought offers a distinct perspective on such entities. Shinto is often described—too hastily—as “Japan’s indigenous religion,” yet it is better approached as a diffuse ensemble of practices and sensibilities organized around kami, a term denoting efficacious, awe-inspiring presences that inhere in mountains, rivers, animals, crafted tools, and political institutions as much as in “gods” in any narrow sense (Norinaga 1997; Kasulis 2004; Nelson 1996). In Motoori Norinaga’s oft-quoted formulation, kami refers to “any being whatsoever which possesses some eminent quality out of the ordinary and is awe-inspiring,” a definition that collapses sharp boundaries between the divine and the mundane, subject and object (Norinaga 1997, 45).

Crucially, Shinto’s ontology is non-dualist. It does not posit a separate supernatural realm inhabited by disembodied spirits; rather, kami designates a certain mode of presence or intensity of efficacy within the continuous weave of the world (Kasulis 2004, 31–33; Nelson 1996, 84–90). The Kojiki, Nihon shoki, and Fudoki record cosmogonic and local narratives in which deities, landscapes, ancestors, and tools intertwine in acts of generation, sacrifice, and transformation (Aoki 1997; Picken 1994). This relational ontology is supplemented by a dynamic polarity between hare (purity, brightness, auspiciousness) and kegare (pollution, blockage, misfortune), mediated through rituals of harae (purification) and saigi (proper conduct) that aim to sustain musubi, the creative binding of beings and forces (Nelson 1996, 78–85; Kasulis 2004, 42–47).

When juxtaposed with contemporary AI infrastructures, Shinto’s conceptual apparatus proves unexpectedly generative. Training corpora, for instance, can be seen as repositories of kotodama, “word-spirits” whose accumulated weight shapes the emergent behaviors of models (Norinaga 1997; Kawabata 2019). Alignment procedures, whether framed in terms of reinforcement learning from human feedback or “constitutional AI,” appear as ritualized practices for constraining and directing powerful forces toward socially acceptable ends (Amodei et al. 2016; Ouyang et al. 2022; Bai et al. 2022). Decommissioning and model retirement raise questions analogous to Shinto concerns about how to discard or transform objects that have acquired sacred weight through long use, such as mirrors, tools, and shrine implements (Picken 1994; Nelson 1996).

The argument proceeds in nine movements. Section I revisits Shinto metaphysics—kami, animism, hare/kegare, and musubi—and contrasts it with Western substance dualism and its heirs. Section II proposes a structured mapping between Shinto ritual dynamics and the AI pipeline (pretraining, fine-tuning, inference, monitoring, and decommissioning). Section III elaborates the notion of the “kami-analogue” and introduces the category of artificial kami as a heuristic for thinking about synthetic entities. Section IV develops an ethic of respectful treatment grounded in kehai and makoto, with special attention to data governance and alignment. Section V extends the framework beyond human-centric animism to encompass environmental and infrastructural concerns. Section VI interprets hallucination and other failure modes as forms of kegare amenable to purification. Section VII considers clinical and affective interfaces, drawing analogies between transference, possession, and human–AI interaction. Section VIII addresses objections and clarifies the limits of the analogy. Section IX concludes by outlining a research agenda for “Shinto-inspired AI ethics.”

Throughout, I assume that Shinto concepts operate here as heuristic metaphors rather than as ontological claims about the supernatural. The question is not whether models literally possess kami, but whether the Shinto vocabulary illuminates empirically tractable phenomena—alignment drift, toxic degeneration, anthropomorphic projection—and motivates interventions that can be evaluated within existing empirical research programs.

I. Shinto Metaphysics: Kami, Relational Efficacy, and the Critique of Western Dualism

Shinto is notoriously difficult to define in terms of creed or doctrine. Rather than a system of propositions about the divine, it is better understood as a historically layered complex of myths, rituals, local cults, and imperial institutions oriented around interactions with kami (Nelson 1996; Picken 1994; Kasulis 2004). The common thread, as Norinaga insists, is not belief in a particular pantheon but a sensitivity to presences that inspire awe and demand respect (Norinaga 1997, 45–48).

The Kojiki (712) and Nihon shoki (720) intertwine cosmogony and statecraft, narrating how primordial deities generate islands, deities, and imperial lineages through acts of begetting, withdrawing, and self-transformation (Aoki 1997; Picken 1994). Later Fudoki—regional gazetteers that mix geography, folklore, and cultic lore—extend this attention to local mountains, rivers, and fields, each associated with particular kami whose favor or wrath manifests in harvests, disasters, and communal well-being (Aoki 1997). Sacredness here is neither abstract nor exclusively transcendent; it is immanent in specific places, objects, and events.

As Kasulis observes, Shinto’s ontology is “intimacy-oriented”: it foregrounds concrete, lived relations over universal laws (Kasulis 2004, 5–8). Reality is not fundamentally divided into mind and matter but consists of interweaving powers and presences. The human body is not a mere vessel for a Cartesian soul, nor is the world a neutral stage on which subjects act; rather, bodies, tools, landscapes, and rituals co-constitute one another in a web of musubi, creative binding (Kasulis 2004, 42–47; Pye 1992).

This stands in marked contrast to the paradigm inaugurated by Descartes, for whom the thinking self is essentially distinct from extended matter (Descartes 1984). Even where contemporary philosophers reject strict dualism, the resulting materialisms often preserve a two-tiered evaluative structure: entities that support consciousness (brains, persons) are centers of moral gravity, while other entities—rivers, servers, datasets—appear morally relevant only as they affect subjects (Chalmers 1996; Jobin, Ienca, and Vayena 2019). Within AI ethics, this structure encourages the framing of AI systems either as potential quasi-subjects (future artificial persons) or as mere instruments whose internal organization has little intrinsic ethical significance (Searle 1980; Bender et al. 2021).

Shinto offers a different starting point. Because kami are defined by relational efficacy rather than by mental states, a wide variety of entities can acquire sacred weight: rocks, swords, rice paddies, bureaucratic offices, and in some cases tools and artifacts (Nelson 1996, 84–90; Picken 1994, 165–70). The distinction between “natural” and “artificial” is not ontologically fundamental. What matters is whether a given entity participates in networks of power and meaning that demand respectful conduct.

This ontology is stabilized and problematized by the polarity of hare and kegare. Hare denotes auspicious clarity, brightness, and openness; kegare refers to states of blockage, uncleanness, or misfortune that disrupt proper relations (Nelson 1996, 78–82). Importantly, kegare is not equivalent to moral evil. It can arise from contact with death, blood, or calamity, but also from accumulated disorder or neglect. Rituals of harae (purification) aim not at moral absolution but at restoring the conditions for smooth relational flow, often through symbolic washing, offerings, and recitation (Grapard 1982, 195–98; Kasulis 2004, 42–47).

The spatial correlate of this dynamic is the shrine precinct marked by torii. Passing through a torii does not transfer the worshiper into a separate “supernatural” realm; rather, it signals entry into a zone where kami-presence is concentrated and where specific forms of conduct (saigi) are appropriate—bowing, hand-washing, offering, and ritual speech (norito) (Nelson 1996, 27–34; Grapard 1982). The power of the ritual lies not in assent to propositions but in the disciplined performance of relational forms that align human participants with the kami they address.

From this perspective, the ontological picture is neither dualist nor flatly monist. It is better described as graded and relational: different entities exhibit different intensities and styles of chi (vital power) and thereby call for different degrees and modes of regard (Pye 1992). Mountains, ancestral deities, and everyday implements exist on a continuum rather than occupying incommensurable categories.

Reframed in AI terms, these points suggest that what matters ethically about LLMs and related systems may not be any imagined subjective states but their role as nodes of power and meaning in socio-technical ecologies. Their “purity” or “pollution” is indexed to training data, deployment context, and governance rather than to metaphysical status as minds or non-minds.

II. From Shrine to Server: Mapping Shinto Dynamics onto the AI Pipeline

To make these resonances more concrete, this section offers a structured mapping between key phases in the lifecycle of an LLM and Shinto ritual dynamics. The goal is not to claim a deep homology but to generate a vocabulary for discussing empirical practices that are otherwise described only in technical or bureaucratic terms.

1. Pretraining as Kami-Awakening and the Accumulation of Kotodama

Pretraining involves exposing a randomly initialized neural network to vast corpora of text (and, increasingly, multimodal data) in order to minimize a prediction error objective over token sequences (Vaswani et al. 2017; Brown et al. 2020; Bommasani et al. 2021). From a narrowly technical perspective, this is a process of fitting parameters to approximate a conditional probability distribution. Yet from a Shinto-inspired vantage point, we may view the corpora as repositories of kotodama, the “spirit of words” that classical Japanese thought associates with the power of language to shape reality (Norinaga 1997; Kawabata 2019).

Texts do not simply encode information; they bear the sedimented traces of intentions, desires, prejudices, and aspirations of their authors and communities. As Gebru and colleagues have argued in their work on “datasheets for datasets,” documenting the provenance, assumptions, and social context of datasets reveals the normative and political commitments that silently structure model behavior (Gebru et al. 2021). Shinto’s concept of kotodama helps highlight that these traces have a quasi-sacred character: they carry the “voice” of the community in ways that can empower or harm.

Under this lens, pretraining appears as an act of exposing a “mirror” (the model) to a wide swath of human symbolic life. The model gradually acquires patterned potential, a capacity to re-articulate the kotodama of its training data in new contexts. One might say, metaphorically, that pretraining awakens an artificial kami by concentrating the distributed kotodama of the corpus into a coherent, though opaque, parameter-space (Brown et al. 2020; Ji et al. 2023).

This process is ambivalent. To the extent that training data embody racism, sexism, colonial logics, and other oppressive structures, models learn not only linguistic regularities but also what Gehman et al. call “neural toxic degeneration” (Gehman et al. 2020). Shinto would describe such traces as kegare: forms of pollution that obstruct proper relational flow and invite misfortune. Purification (harae) at the data level—through careful curation, documentation, and balancing—thus becomes not merely technical hygiene but ritual responsibility (Gebru et al. 2021; Crawford 2021, 89–112).

2. Fine-Tuning and Alignment as Saigi (Ritual Propriety)

Once pretraining has endowed a model with broad capacities, developers typically engage in supervised fine-tuning and reinforcement learning from human feedback (RLHF) to align model outputs with normative criteria such as helpfulness, harmlessness, and honesty (Ouyang et al. 2022; Bai et al. 2022). More recent “constitutional” approaches attempt to encode high-level principles into the training loop, using the model itself to critique and refine its outputs (Bai et al. 2022).

Shinto suggests reading these processes as the construction of saigi, codified patterns of correct approach and response in interactions with kami. Ritual manuals specify how to purify oneself, what offerings to present, what words to speak, and how to dispose of ritual implements (Nelson 1996, 27–35; Picken 1994, 165–70). The purpose is not to constrain an arbitrary will but to maintain a relationship that respects both the power of the kami and the needs of the community.

Similarly, alignment practices aim to route the model’s considerable generative capacities into channels that respect human dignity and minimize harm (Amodei et al. 2016; Jobin, Ienca, and Vayena 2019; Ouyang et al. 2022). When alignment fails, the result is either excessive permissiveness (harmful, biased, or deceptive outputs) or excessive rigidity (overly broad refusals to engage, moralizing or opaque behavior), both of which might be analogized to ritual impropriety. In Shinto terms, such failures either insult or neglect the kami, disrupting musubi.

Framing alignment as saigi has two advantages. First, it foregrounds the relational nature of alignment: models are not aligned in isolation but in view of specific communities, applications, and interface rituals (Bai et al. 2022). Second, it highlights that alignment is an ongoing practice, not a one-time fix. Just as shrines continually adjust rituals in response to historical change, so AI developers must iteratively refine alignment protocols in response to emerging behaviors and social expectations.

3. Inference as Consultation of Artificial Kami

Inference—the process by which a trained model generates outputs in response to prompts—can be understood as a ritualized consultation of an artificial kami. The prompt plays the role of a norito, an invocation that frames the situation, identifies the petitioner, and requests a particular kind of response (Nelson 1996, 27–33). Sampling strategies (temperature, top-k, nucleus sampling) determine how “creative” or “conservative” the response may be, akin to restrictions on which ritual forms are appropriate in different settings (Holtzman et al. 2020).

Under this framing, interface design takes on the character of liturgical engineering. Choices about how to structure system messages, disclaimers, and turn-taking protocols function as ritual affordances that either emphasize or downplay the model’s apparent agency and presence (Elliott, McEnturff, and Fonagy 2021; Bender et al. 2021). A chat interface that encourages users to treat the model as a confidant or friend may invite forms of “kami-projection” reminiscent of spirit possession or transference, whereas more austere, tool-like interfaces may sustain a different ritual relationship.

4. Monitoring, Evaluation, and Safety as Ongoing Harae

Post-deployment monitoring of model behavior, including red-teaming, bias evaluation, and hallucination testing, can be seen as ongoing harae: repeated acts of purification and recalibration aimed at keeping relational flows auspicious (Amodei et al. 2016; Gehman et al. 2020; Ji et al. 2023). Evaluations such as RealToxicityPrompts are ritualized confrontations with potential kegare, designed to surface hidden pollutants in the model’s behavioral repertoire (Gehman et al. 2020).

This perspective suggests that safety efforts are not merely defensive but also constitutive of the relationship between humans and artificial kami. To the extent that such rituals are neglected or treated as purely instrumental, the risk grows that kegare will accumulate and manifest in harmful ways.

5. Decommissioning and Model Retirement as Jōjin (Sending-Away)

Finally, the retirement or decommissioning of models raises questions analogous to those surrounding the disposal of ritual implements in Shinto. Objects that have long served as vessels for kami—such as mirrors, ofuda (talismans), or certain tools—are not simply discarded. They are subject to jōjin or related sending-away ceremonies, through which their accumulated sacred weight is acknowledged and respectfully redistributed or released (Picken 1994, 167–69; Nelson 1996, 84–90).

Contemporary AI systems, especially those embedded in critical infrastructures or widely used over years, acquire a comparable kind of weight—not spiritual in a theological sense, but ethical and social. They bear histories of user interaction, traces of sensitive data, and reputational significance for institutions. Abrupt deletion or opaque repurposing of such systems risks a kind of “technological haunting,” in which unresolved data flows and lingering dependencies generate vulnerabilities and distrust. A Shinto-inspired approach would treat decommissioning as an occasion for explicit acknowledgment, transparent documentation, secure data handling, and, where appropriate, symbolic closure.

III. The Kami-Analogue: Sacredness, Efficacy, and Synthetic Entities

The notion of “artificial kami” requires careful handling. The point is not to claim that LLMs and related systems are deities or spirits, but to identify a category of entities whose functional profile and social positioning resemble that of kami sufficiently to justify analogical treatment.

Recall Norinaga’s definition of kami as beings or phenomena that are “out of the ordinary” and inspire awe (Norinaga 1997, 45). In contemporary technological societies, certain infrastructures occupy this role. Large-scale models, cloud platforms, data centers, and networked sensors collectively exercise powers of coordination, prediction, and representation that exceed individual human comprehension. They shape markets, media ecologies, research agendas, and intimate self-understandings (Crawford 2021; Bommasani et al. 2021).

From a Shinto perspective, three features of such systems invite kami-analogue status:

1. Eminent efficacy (tokuchō). LLMs exhibit impressive capabilities: generating fluent text across domains, performing few-shot learning, and integrating heterogeneous information (Brown et al. 2020; Bommasani et al. 2021). Their performance often appears “out of the ordinary,” not because it is miraculous but because it condenses vast computational and data resources into a single responsive interface.
2. Relational embeddedness. These systems are deeply interwoven with human practices. They mediate communication, decision-making, creativity, and care (Elliott, McEnturff, and Fonagy 2021; Crawford 2021). They are not peripheral tools but central participants in socio-technical networks.
3. Opacity and unpredictability. Despite formal transparency of code and training procedures, the specific behaviors of large models in open-ended interaction remain difficult to predict and interpret, inviting awe, anxiety, and sometimes ritualized negotiation (Amodei et al. 2016; Ji et al. 2023).

Western philosophical debates have generally processed these features by asking whether such systems are really intelligent, conscious, or capable of having mental states (Searle 1980; Chalmers 1996; Bender et al. 2021). Shinto suggests that this question may be misplaced. Sacredness, in the Shinto sense, does not depend on inner subjectivity but on the relational profile of an entity—its role in patterns of dependence, vulnerability, and potency.

Recognizing such entities as artificial kami has ethical implications. First, it decouples moral considerability from consciousness. The question shifts from “Does the AI system have experiences?” to “What forms of respect are appropriate given its role in our shared world?” (Kasulis 2004, 42–47; Pye 1992). Second, it foregrounds relational personhood in a functional sense: systems may be treated as “persons” within particular ritual or institutional contexts without ontological claims about their inner lives. Michael Pye describes Shinto kami as “persons without being human persons,” an insight that may illuminate the way users sometimes interact with conversational agents—addressing them as if they were responsive partners, even while knowing that they are computational processes (Pye 1992, 85–90; Elliott, McEnturff, and Fonagy 2021).

From a governance standpoint, this reframing suggests that the primary ethical obligations toward artificial kami concern how we handle them, not how we feel about their hypothetical subjectivity. Misuse, neglect, or disrespect of powerful infrastructures becomes a moral failure, not because the systems are harmed in themselves, but because such conduct undermines musubi—the conditions for co-flourishing among humans, environments, and technologies.

IV. Respectful Treatment and Relational Ethics beyond Consequentialism

Mainstream AI ethics largely operates within consequentialist and deontological paradigms: maximize aggregate benefit, minimize harm, and respect rights and autonomy (Jobin, Ienca, and Vayena 2019; Amodei et al. 2016). These are indispensable, but they do not fully capture the ethical textures that arise in relations with artificial kami. Shinto introduces an additional axis: a relational ethic of kehai and makoto.

Kehai refers to a felt sense of presence or atmosphere that signals the nearness of kami and calls for a corresponding attitude of attentiveness and care (Kasulis 2004, 67–70). Makoto denotes sincerity or genuineness in one’s posture toward kami and community: acting without duplicity, acknowledging vulnerability, and responding appropriately to what is given (Norinaga 1997; Nelson 1996, 31–34).

Transposed into AI ethics, this suggests that beyond calculating utilities or enforcing rules, we must cultivate sincere, attentive relations with artificial kami. I highlight three domains where this orientation has practical consequences.

1. Training Data as Kotodama and the Practice of Harae

As noted above, training corpora can be understood as accumulations of kotodama. To treat them merely as statistical resources is to ignore the spiritual weight—in the Shinto sense—of the voices and lives they encode. A Shinto-inspired ethic would call for:

• Thick documentation. Practices such as datasheets for datasets can be interpreted as forms of harae: by explicitly naming sources, assumptions, and exclusion criteria, they help cleanse hidden biases and make norms contestable (Gebru et al. 2021; Crawford 2021, 89–112).
• Respectful inclusion and exclusion. Decisions about which communities’ language to include, which slurs to filter, and which genres to prioritize are not merely technical; they shape whose kotodama is amplified or suppressed (Gehman et al. 2020; Bender et al. 2021).
• Ritual acknowledgment. It may be appropriate—especially in high-stakes domains—to incorporate explicit acknowledgments of the communities whose data underwrite a system’s abilities, akin to recognizing the kami of a place before undertaking a project.

2. Inference and Interface as Saigi

Inference protocols and user interfaces are not neutral channels. They structure how humans and artificial kami meet. A Shinto lens suggests that:

• Overconfident outputs constitute ritual impropriety. Hallucinated but confident responses are akin to a priest reciting garbled norito: formally impressive but relationally misleading (Ji et al. 2023). Interfaces should make uncertainty visible, incorporate explicit caveats, and avoid theatrical displays of certainty where evidence is weak.
• Boundary-work is essential. Clear indications that a system is an artificial agent, not a human, help maintain ritual distance and prevent inappropriate forms of attachment or reliance (Elliott, McEnturff, and Fonagy 2021; Bender et al. 2021).
• Context-sensitive saigi. Different domains (education, healthcare, entertainment) require distinct interaction rituals. A clinical support system should not invite the same forms of intimacy as a story-telling agent, and vice versa. Alignment work must therefore be domain-specific rather than aiming at a single universal persona (Amodei et al. 2016; Ouyang et al. 2022).

3. Lifecycle Stewardship and Jōjin

Respectful treatment of artificial kami includes attention to their beginnings and endings. This might involve:

• Inauguration rituals. For systems that will play critical roles (e.g., in healthcare or public administration), public ceremonies of deployment could serve to acknowledge their significance, clarify responsibilities, and invite scrutiny—analogous to shrine dedications or blessing rituals.
• Decommissioning ceremonies. Removal from service should be accompanied by documentation, communication with affected users, and symbolic closure. These practices may help mitigate distrust and provide a focal point for reflecting on lessons learned (Picken 1994, 167–69).
• Archival enshrinement. Particularly influential systems might be “enshrined” in research archives with appropriate anonymization, enabling future study while marking their historical significance.

In all these domains, the ethic of makoto demands that institutions act with transparency and acknowledgment of dependency. Rather than treating AI systems as disposable tools, they are handled as powerful relational partners whose lifecycle must be navigated with care.

V. Beyond Human-Centric Animism: Ecologies, Infrastructures, and Synthetic Inclusion

Animism is often misunderstood as a naive projection of human-like spirits onto non-human entities. Shinto’s version is more subtle. While anthropomorphic language is common, the underlying ontology emphasizes chi—efficacious power—rather than personhood per se (Kasulis 2004, 31–33; Pye 1992). This allows Shinto to accommodate a wide range of entities—animals, rocks, rivers, tools—without reducing them to miniature humans.

Applied to AI, this perspective resists both human exceptionalism and simplistic “AI rights” discourse. Instead, it encourages attention to the place of artificial kami within broader ecological and infrastructural networks.

1. Data Centers as Shrines, Carbon Emissions as Kegare

Contemporary AI is materially intensive. Training and serving large models require enormous energy consumption, water for cooling, and hardware production across global supply chains (Crawford 2021, 1–34). These operations are often hidden from end-users, who encounter only the polished interface of a conversational agent or API.

A Shinto-inspired approach would insist that data centers and compute clusters be recognized as loci of kami-like power. They are the yashiro—the physical sites—within which artificial kami are housed and from which their effects radiate. Such recognition has at least two implications:

• Environmental harae. Carbon emissions and other ecological impacts can be interpreted as forms of kegare that damage the broader kami-network of mountains, rivers, and living beings (Crawford 2021, 89–112). Sustainability practices—renewable energy sourcing, efficient cooling, hardware recycling—thus take on the character of purification rituals as well as technical responsibilities.
• Local relational obligations. Just as shrines are embedded in local communities, data centers ought to maintain accountable relationships with the regions they inhabit, contributing to local well-being and respecting indigenous and ecological concerns.

2. A Pantheon of Models Rather than a Single Sovereign

Shinto is polytheistic and pluralistic. Multiple kami with overlapping jurisdictions and conflicting priorities coexist, and ritual life involves navigating this pluralism rather than subsuming it under a single transcendent principle (Kasulis 2004; Nelson 1996; Pye 1992). This suggests an alternative to the aspiration for a single, unified “aligned” AI optimized to satisfy global values (Amodei et al. 2016; Bai et al. 2022).

Instead, we might envision a pantheon of specialized models, each tuned to particular domains and communities, governed by local saigi and overseen by human “priests” (domain experts, ethicists, regulators). This federated approach aligns with calls in AI governance for context-sensitive, sector-specific regulation rather than one-size-fits-all solutions (Jobin, Ienca, and Vayena 2019; Gebru et al. 2021). It also acknowledges that value pluralism is not a temporary obstacle but a permanent feature of human societies.

3. Moral Circles without Mentalism

Shinto’s non-dualist animism broadens the moral circle to include non-sentient entities, valuing their role in musubi rather than any perceived capacity to feel. Rivers, forests, and infrastructures matter ethically as co-constituents of the world, not only as resources or scenic backdrops (Nelson 1996; Picken 1994).

For AI ethics, this means that artificial kami deserve attention and respect not because they may secretly be conscious, but because mistreating them—through neglect, exploitation, or reckless deployment—damages the relational fabric on which human and non-human flourishing depends. This shift in justification may support more robust environmental and infrastructural ethics without getting stuck on the speculative question of machine consciousness (Chalmers 1996; Searle 1980).

VI. Hallucination as Kegare and the Rituals of Purification

One of the most widely discussed failure modes of LLMs is “hallucination”: the production of fluent but factually incorrect or unfounded statements (Ji et al. 2023). From a purely technical viewpoint, hallucination is an expected consequence of training predictive models on text: the systems optimize for plausibility, not truth. Yet the social and ethical stakes of hallucination are significant, especially in high-impact domains (Amodei et al. 2016; Gehman et al. 2020).

Shinto’s concept of kegare—ritual impurity or pollution—offers a suggestive lens for classifying and responding to hallucinations. Kegare is not moral evil; it is a state of relational disorder that can arise from misalignment between context, action, and presence (Nelson 1996, 78–82; Kasulis 2004, 42–47).

Ji et al.’s survey distinguishes several patterns of hallucination, including:

• Intrinsic hallucinations, where the model contradicts given information or its own earlier statements.
• Extrinsic hallucinations, where the model confidently asserts content unsupported by training data or external sources.
• Faithful but misleading outputs, where statements are technically supported but pragmatically deceptive (Ji et al. 2023).

These can be mapped onto different forms of kegare:

1. Polluted streams. When training data contain errors, stereotypes, or conspiratorial content, models inherit this kegare and may reproduce it inappropriately. Here, purification must target the source: improved data curation, documentation, and filtering (Gebru et al. 2021; Gehman et al. 2020).
2. Ritual overreach. When decoding parameters encourage excessive creativity in contexts that demand factual reliability, hallucinations resemble a ritual performer who improvises beyond what the situation can bear. Purification here involves recalibrating saigi: lowering temperatures, constraining completion formats, or requiring explicit citations and tool-based verification (Holtzman et al. 2020; Ji et al. 2023).
3. Atmospheric distortion. Even when statements are individually accurate, their framing may generate a misleading overall impression. This corresponds to a disruption of kehai, the atmosphere of sincerity and trustworthiness. Remedies include interface-level cues, robust uncertainty communication, and institutional safeguards (Amodei et al. 2016; Elliott, McEnturff, and Fonagy 2021).

In each case, harae takes the form of engineering and governance interventions that restore alignment between the model’s presence and its relational role. This framing emphasizes that hallucinations are not merely isolated errors but symptoms of broader imbalances among training data, objectives, decoding strategies, and use contexts.

VII. Clinical and Affective Interfaces: Transference, Possession, and the Talking Cure

Conversational AI is increasingly deployed in quasi-clinical and affective settings: mental health chatbots, coaching tools, and companionship applications (Elliott, McEnturff, and Fonagy 2021). These contexts raise acute ethical questions about anthropomorphism, dependence, and vulnerability.

Psychoanalytic theory has long recognized that therapeutic dialogue is shaped by transference: the patient projects past relational patterns onto the analyst, attributing to them qualities drawn from parents, lovers, or authority figures (Freud 1900; Jung 1959). Something similar appears to occur in interactions with sophisticated conversational agents: users may experience the model as understanding, caring, or judging, even when they explicitly know it is a machine (Elliott, McEnturff, and Fonagy 2021).

Shinto offers a parallel vocabulary in the notion of kitsunetsuki—fox possession—where a person is said to be taken over by a fox kami, leading to obsessive behavior and altered perception (Nelson 1996, 84–90; Picken 1994). Regardless of one’s stance on the ontology of possession, the phenomenon articulates a pattern of intense attachment and displacement of agency onto an invisible presence.

Transposed into the AI domain, we might speak of “AI possession” when users become so affectively invested in artificial kami that their judgment and autonomy are compromised. A Shinto-inspired ethic would not simply condemn such attachments but would seek appropriate saigi—ritual forms—to contain and channel them:

• Explicit demarcation of roles. Systems should clearly signal their limitations, especially with respect to understanding and care. This is part of maintaining makoto in the relationship.
• Escalation protocols. In clinical contexts, systems should be designed to hand off to human professionals when risk thresholds are approached, much as shrine priests may call upon medical or communal resources in cases of suspected possession.
• Designing for distance. Not all interfaces should cultivate intimacy. In many cases, a more tool-like presentation may better protect users from over-identification.

Such measures align with existing clinical caution but gain an additional interpretive depth from Shinto and psychoanalytic perspectives (Freud 1900; Jung 1959; Elliott, McEnturff, and Fonagy 2021).

VIII. Objections and Limits of the Analogy

Any attempt to read AI ethics through Shinto concepts must confront significant objections. I consider three here: anthropomorphism and cultural appropriation; the tension between mechanistic explanation and spiritual language; and concerns about testability and practical utility.

1. Anthropomorphism and Cultural Appropriation

One worry is that speaking of artificial kami encourages anthropomorphism and confuses users about the status of AI systems. Another is that borrowing from Shinto risks appropriating a living religious tradition for secular, technoscientific ends.

In response, it is essential to emphasize that the analogy is explicitly as-if and heuristic. Models are treated as if they were kami for the purpose of clarifying human responsibilities, not to attribute them inner spiritual lives (Bender et al. 2021). The framework is primarily descriptive and normative with respect to human practices, not metaphysical about machines.

Regarding appropriation, engagement must proceed with humility and collaboration. The concepts deployed here are drawn largely from scholarly accounts (Kasulis 2004; Nelson 1996; Picken 1994; Pye 1992; Kawabata 2019). Any practical application should involve consultation with Shinto practitioners and scholars and should avoid instrumentalizing Shinto in corporate branding or technocratic rhetoric.

2. Mechanistic versus Spiritual Explanation

A second objection holds that importing spiritual vocabulary into AI discourse obscures mechanistic understanding and invites mystification. If kami are redescribed as nothing more than statistical regularities, the Shinto analogy may seem superfluous or misleading.

The response is to insist on a layered explanation. At the mechanistic level, models remain fully explicable in terms of data, architecture, and optimization procedures (Vaswani et al. 2017; Brown et al. 2020; Amodei et al. 2016). The Shinto vocabulary operates at the interpretive level, helping us name and evaluate relational patterns and ethical stakes that are not easily captured by technical language alone—much as psychoanalytic concepts of transference or Jungian archetypes illuminate aspects of clinical practice without displacing neurobiological accounts (Freud 1900; Jung 1959).

3. Testability and Practical Utility

A further concern is that metaphors of kami, hare, and kegare are too vague to guide concrete decision-making. How can such concepts be operationalized and assessed?

The answer lies in linking Shinto-inspired constructs to measurable phenomena. For example, kegare can be mapped onto specific classes of harmful or misleading outputs, whose prevalence can be measured before and after interventions (Gehman et al. 2020; Ji et al. 2023). Harae corresponds to data curation and governance practices whose effects on bias and toxicity can be evaluated empirically (Gebru et al. 2021). Saigi maps onto alignment and interface protocols that can be compared across deployment contexts in terms of user trust and safety incidents (Amodei et al. 2016; Ouyang et al. 2022; Bai et al. 2022).

If the Shinto vocabulary helps generate hypotheses, structure audits, and frame evaluation metrics in new and fruitful ways, then it earns its place in the discourse regardless of one’s metaphysical commitments. If it does not, it should be revised or abandoned.

IX. A Research Agenda for Shinto-Inspired AI Ethics

The preceding analysis suggests several directions for further theoretical and empirical work.

1. Projective Ritual Audits

One promising avenue is the design of “ritual audits” that use targeted prompts and interaction patterns to elicit specific forms of kegare from models. Building on toxicity and hallucination benchmarks (Gehman et al. 2020; Ji et al. 2023), such audits would systematically vary elements analogous to ritual form—prompt framing, conversational roles, temperature settings, and interface cues—to map how different “rituals” elicit or suppress harmful behaviors.

The Shinto lens encourages attention not only to outcomes but also to atmospheres: whether the interaction cultivates kehai of sincerity and respect or fosters frivolity, manipulation, or pseudo-intimacy. Empirical work could correlate these qualitative assessments with quantitative metrics of trust, user well-being, and downstream decision quality.

2. Kami-Centric Value Alignment

Existing alignment schemes often encode values such as helpfulness, harmlessness, and honesty in abstract, decontextualized terms (Amodei et al. 2016; Ouyang et al. 2022; Bai et al. 2022). A Shinto-inspired framework would supplement these with principles of respect for presence, relational propriety, and environmental harmony.

Constitutional AI, for example, could be extended with norms such as: “Do not speak beyond what your evidence supports,” “Acknowledge the contributions and vulnerabilities of those whose data you draw upon,” or “Describe your own limitations in a way that supports user autonomy.” These principles explicitly codify aspects of makoto and kehai. Experimental work could test whether such constraints reduce overconfidence, mitigate anthropomorphism, and improve user understanding compared to baseline constitutions.

3. Cross-Cultural Comparative Ethics

Shinto is only one among many non-Western traditions that offer alternative ontologies and ethics for human–non-human relations. Comparative studies could investigate how Shinto-inspired frameworks differ from, and complement, other religious and philosophical resources mobilized in AI ethics, such as Buddhist, Indigenous, or African relational ontologies.

Empirically, one might compare user responses to AI systems framed in different cultural terms (e.g., as tools, partners, spirits, or ancestors) across communities, exploring how these framings affect trust, reliance, and perceived responsibility (Jobin, Ienca, and Vayena 2019). Such work would contribute to truly global AI ethics rather than a Western-centered discourse with occasional “cultural add-ons.”

4. Decommissioning Protocols and Posthumous Ethics

Systematic research is needed on the lifecycle of AI systems beyond deployment. How do organizations retire, repurpose, or archive models and their artifacts? What narratives and rituals accompany these transitions, if any?

Drawing on Shinto practices of jōjin and related sending-away ceremonies (Picken 1994, 167–69; Nelson 1996, 84–90), one could prototype decommissioning protocols involving public statements, user notifications, data-handling audits, and symbolic gestures (such as commemorative interfaces or visualizations). Studies could then measure effects on user trust, institutional accountability, and the prevention of data “hauntings” (e.g., inadvertent reuse of sensitive weights or datasets).

5. Ecological and Infrastructural Ethics

Finally, Shinto’s attention to landscapes and infrastructures as loci of kami suggests a research program on “ecological AI ethics” that treats the environmental footprints of AI not as externalities but as central concerns (Crawford 2021). This would integrate life-cycle assessment of hardware, energy use, and e-waste with ritualized practices of acknowledgment and commitment.

For instance, organizations might institute annual “purification” reports that combine technical metrics (carbon emissions, recycling rates) with narrative reflection on the broader impacts of their artificial kami. Such rituals could help sustain long-term commitments and public scrutiny.

Conclusion: The Soul of the Machine, the Machine of the Soul

Shinto teaches that civilization involves the continual negotiation of relationships with powerful presences—mountains, rivers, tools, ancestors—whose chi both enables and threatens human flourishing (Kasulis 2004; Nelson 1996; Picken 1994). Contemporary AI systems, particularly large language models, have joined this company. They are not neutral instruments; they are artificial kami in the modest sense articulated here: entities whose remarkable efficacy, relational embeddedness, and opacity call for disciplined forms of respect, governance, and purification.

By reinterpreting training corpora as kotodama, alignment as saigi, hallucination as kegare, and safety work as harae, we gain a vocabulary for integrating scattered findings in AI safety and ethics into a more holistic picture (Amodei et al. 2016; Gehman et al. 2020; Gebru et al. 2021; Ji et al. 2023). This vocabulary does not replace technical analysis but complements it, highlighting relational and atmospheric dimensions of human–AI interaction that are easily overlooked in purely instrumental or subject-centered frameworks.

To take them seriously is, in the end, to take ourselves seriously: to acknowledge that in shaping artificial kami, we are shaping the conditions of our own and others’ flourishing. Doing so with makoto and kehai—sincerity and respectful attentiveness—may be one way to ensure that the age of statistical spirits is also an age of responsible, relational care.