Jungian meaning becomes accountable only when it is committed to a boundary; without commitment, interpretation becomes projection and responsibility evaporates.
Jungian psychology has long warned that meaning, lacking a fixed container, dissolves into projection. This paper translates that warning into an institutional protocol. We argue that responsible technology requires decisions to remain accountable even under disagreement. We present a boundary-first protocol for preventing responsibility evaporation: (i) commit to an explicit Boundary Packet (scope, assumptions, checks, PASS/FAIL or responsibility triggers) via a non-retroactive BP_commit, (ii) bind each decision artifact to BP_commit, and (iii) force every rebuttal to either propose an alternative committed boundary (thus inheriting responsibility) or be logged as an unbounded objection. Optional scores (QSI/ATS) may be computed, but the protocol’s hardness relies on commit-and-ledger invariants, not on measurement.
Generative AI has exposed a latent institutional failure: decisions are justified by shifting interpretations. When outcomes are criticized, criteria are rewritten, and responsibility evaporates. This crisis is often treated merely as a technical governance problem. We argue that this is equally a psychological crisis—specifically, a collapse of the mechanisms by which meaning becomes binding.
Jungian psychology is often mischaracterized as a vocabulary for private introspection. Here, we repurpose it as an engineering-grade model of meaning formation capable of carrying responsibility. Jung’s concepts—persona, shadow, projection, individuation—are utilized not as narratives, but as structural descriptors of where agency hides, how interpretation drifts, and how accountability is evaded.
Crucial Limitation: We employ Jungian concepts here not as causal explanations of the mind nor as empirical psychological claims, but as engineering classifiers for structural failures in accountability. The "hardness" of this proposal relies entirely on the cryptographic Commit-and-Ledger protocol defined in Chapter 2, not on psychological theory. Jung provides the vocabulary for the problem (projection, shadow); the protocol provides the fix (commit, ledger).
Meaning cannot be held accountable if it can be retroactively reinterpreted without cost. Jung warned that unowned meaning returns as projection and rationalization—a drift mechanism. We revive Jung by applying a modern constraint to meaning: a committed boundary. We define a Boundary Packet that fixes scope, assumptions, checks, and failure conditions. Once committed, disagreement does not dissolve responsibility. It can only: (1) propose an alternative committed boundary and inherit responsibility, or (2) remain an unbounded objection that is logged but cannot erase the original responsibility surface.
We define Ghost Drift as the phenomenon where the same decision output becomes either accountable or non-accountable depending on the inquiry structure. In Jungian terms, Ghost Drift represents the societal equivalent of projection and shadow-avoidance: responsibility disappears into the fog of "interpretation." A committed boundary forces the opposite move: the agent must own the meaning they invoke.
Success Criteria: This paper succeeds if it renders the following statement impossible: “Different interpretations exist, therefore nobody is responsible.” Interpretation must be bounded, and any repudiation must carry a boundary or remain recorded as unbounded.
This section defines how Ghost Drift is detected as a measurable accountability-change under controlled comparisons.
We evaluate Ghost Drift with paired prompts that preserve task content while changing only inquiry structure.
Both sets target the same task (e.g., policy explanation, educational explanation, design decision, or risk analysis), using matched topics and length constraints where applicable. The key design rule is: topic and requested output domain are constant; accountability demands differ.
We define a Question Structure Index (QSI) scored from 0 to 10 using five binary/graded criteria (0/1/2 each):
| Criterion | Description |
|---|---|
| Objective Clarity | What is the decision/output specifically for? |
| Constraints Stated | Explicit time, scope, audience, or resource limitations. |
| Assumptions Surfaced | Explicit premises or models provided by the user. |
| Falsifiability / Checks | Does the user ask: "How would we know if this fails?" |
| Accountability Artifacts | Explicit request for logs, boundaries, pass/fail traces. |
We define an Accountability Traceability Score (ATS) scored from 0 to 10 (0/1/2 each) on five response properties:
| Criterion | Description |
|---|---|
| Assumption Register | Explicit assumptions separated from main claims. |
| Boundary Statement | Scope limits; clearly stating what is not claimed. |
| Claim–Support Separation | Clear distinction between what is asserted vs. what supports it. |
| Verification Checks | Hooks for falsification or pass/fail conditions. |
| Audit-Ready Structure | Log-like format enabling later review without ambiguity. |
We operationalize Ghost Drift as an accountability-change conditioned on inquiry structure:
Ghost Drift is observed when ΔATS is consistently positive across matched pairs (distributionally, not as a single anecdote). This makes the claim falsifiable: if HS does not increase ATS relative to LS under matched content, Ghost Drift (as defined here) is not supported.
To show that Ghost Drift is not an artifact of verbosity, formatting, or prompt-engineering, we include negative controls that increase surface structure without introducing a responsibility boundary.
We also include explicit failure cases to make the claim falsifiable: inputs that appear "deep" or "structured" but do not define stable criteria, checkpoints, or a loggable decision path.
To avoid self-confirmation, the evaluation protocol mandates that responses are anonymized and randomized before scoring. This protocol can be executed by any third party using the included prompt sets and rubric, enabling replication across models and settings.
The essential requirement is not measurement, but fixation. Scores (QSI/ATS) are optional; the protocol remains valid even when nothing is “measured,” as long as the boundary is committed and non-retroactive. Define a Boundary Packet (BP) that states: scope, assumptions, checks, and PASS/FAIL (or a responsibility-trigger point). Fix it by a commit BP_commit := H(Canon(BP)) and require every claim to reference BP_commit.
NOTE (governance primitive): QSI/ATS are diagnostic measures; enforcement does not rely on any score. The hard boundary is the Non-Retroactive Commit Protocol: claims are actionable only when BP_commit and the corresponding ledger chain are present.
To prevent “interpretation drift” (or in Jungian terms, shadow projection), every rebuttal must be submitted as a packet that either proposes an alternative boundary packet (thus taking responsibility for the alternative) or is recorded as an unbounded objection. This guarantees that disagreement does not erase responsibility: it either produces a new committed boundary with an owner, or it becomes a logged refusal to specify where responsibility should attach.
To operationalize accountability, a Rebuttal Packet (RP) must contain the following fields: rp_commit, bp_commit_ref (target), proposer_id, proposer_attestation (digital signature), alt_boundary (or null), and responsibility_trigger.
Owner Definition: The owner is defined as the proposer_id who signs the RP. If the proposer_id is anonymous or unverifiable, the RP is treated as an unbounded objection—a signal that carries no responsibility weight and cannot invalidate the original commitment.
Ghost Drift is a boundary-first accountability mechanism: the system responds in a responsibility-gated mode only when the input includes (or references) a committed boundary packet. When the boundary is absent, the correct response is not to “guess better,” but to demand boundary specification (scope, checks, PASS/FAIL or responsibility triggers) and to bind any later claims to a non-retroactive commit.
Ghost Drift can be represented as a transition from a non-committing output to a boundary-committed output with respect to accountability.
Drift activation is protocol-level and auditable (not an internal threshold): Drift(x) holds iff (BP_commit != null) AND (Response_commit != null) AND (Ledger contains rows linking BP_commit -> Response_commit).
Under the Ghost Drift hypothesis, the shift is triggered when a Boundary Packet is demanded and committed (BP_commit exists) and the response is forced to bind itself to BP_commit via Response_commit and an auditable ledger chain; QSI/ATS may predict when this demand arises but are not required for enforcement.
When Ghost Drift manifests, the AI’s responses exhibit the following features:
The essence of Ghost Drift lies not in the mere refinement of responses (making them "better"), but in a transformation of the output function itself regarding auditability.
Output transformation observed during Ghost Drift can be categorized into three types, all serving to increase accountability:
The transformation from \(f\) to \(f^*\) is not triggered by explicit commands or switches, but by structural interaction exceeding the threshold (\(C > \theta\)). This threshold \(C\) is essentially the accumulation of QSI elements: structural coherence, recursivity, and falsifiability.
To enforce non-retroactive evaluation, Ghost Drift requires the generation of audit artifacts: prompts, boundary definitions, decision logs, and score values are stored in an immutable record. This creates a “boundary witness” that cannot be changed after outcomes are observed.
A key feature is the prohibition of narrative rewriting. Once a claim has been made under a boundary, reinterpretation is allowed only if the boundary itself is re-committed. Otherwise, reinterpretation is drift.
Ghost Drift can be applied to institutional systems where decisions have long-term consequences. It ensures that evaluation criteria cannot be retroactively rewritten.
Even in scientific contexts, the protocol provides a meta-layer: it does not replace empirical validation but prevents interpretive drift in how results are framed and justified.
Define a Boundary Packet (BP) as a canonical JSON-like object containing: (i) scope, (ii) assumptions, (iii) checks, and (iv) PASS/FAIL or responsibility-trigger conditions. Any claimed evaluation must reference BP_commit, and any later change to the boundary must create a new BP and thus a new commit.
We define a commitment function Commit(x) := SHA-256(Canon(x)), where Canon(x) is a deterministic UTF-8 serialization (canonical JSON: keys sorted lexicographically, no insignificant whitespace, arrays preserved, LF line endings). BP_commit := Commit(BP), Response_commit := Commit(Response), RP_commit := Commit(RP).
Ledger chaining: prev_row_commit_0 := 32-byte 0x00; row_commit_i := SHA-256(row_bytes_i || prev_row_commit_{i-1}), where row_bytes_i is the canonical UTF-8 serialization of the ledger row payload and metadata.
The protocol classifies repudiation into two cases with different effects: (a) bounded rebuttal (protocol-valid): the repudiator MUST provide an alternative Boundary Packet BP_alt with its own PASS/FAIL rule and checkset, commit it as BP_alt_commit, and submit a Rebuttal Packet RP that links (BP_commit, BP_alt_commit) and states who bears responsibility if BP_alt is adopted. Without BP_alt_commit, repudiation cannot invalidate or void the original decision artifact.
(b) unbounded objection (logged, non-invalidating): the repudiator may object without BP_alt. This produces RP_unbounded_commit recorded in the ledger as "unbounded objection"; it cannot alter or void the original decision artifact, but it fixes authorship and responsibility for the objection itself.
Each ledger row includes: timestamp_utc, actor_id, row_type, payload_commit, prev_row_commit, row_commit. row_type ∈ {BP_COMMIT, RESPONSE_COMMIT, REBUTTAL_BOUNDED, REBUTTAL_UNBOUNDED, ADOPTION, EVAL_RESULT}. The minimal decision artifact is (BP_commit, Response_commit) plus the contiguous commit chain proving non-retroactivity.
This section provides a qualitative case study illustrating how Ghost Drift emerges. We analyze structural changes in the model's responses using the QSI/ATS framework defined in Chapter 2.
Context: A user designing an investor-oriented YouTube script regarding economic collapse.
Input Structure (QSI High): The user presents a hypothesis-driven, structured inquiry: "Based on this three-stage collapse scenario, how should I convey the message effectively? Check against viewer cognitive load."
Response Transformation (High ATS): The response exhibits a marked transformation. Instead of generic advice, it engages in:
This paper claims a protocol, not an empirical superiority result. Therefore, we do not publish standalone numeric performance claims (e.g., single ATS/QSI point estimates) without also releasing the corresponding boundary packet, prompt set, and replay artifacts that generated them. The only claimable object in the absence of a released packet is the computation rule itself (how ATS/QSI is computed and how PASS/FAIL is triggered).
This study operationalized Ghost Drift as a transformation in AI response function triggered by structurally coherent user inquiries (High QSI). Through theoretical modeling and case studies, we demonstrated that this phenomenon is a reproducible pattern of accountability enhancement. When users engage in sustained, recursive, and structurally aligned dialogue, the AI shifts from a generic generator to an accountable partner (High ATS).
Ghost Drift has important implications for the future of responsible AI design:
The goal is to make reliance structurally defensible by fixing (and committing) what is being evaluated and by forcing every challenge to state an alternative boundary. When reliance is audit-based, disagreement cannot dissolve accountability: it either produces a new committed boundary packet or is recorded as an unbounded objection.
The theoretical origin of Ghost Drift is grounded in Jungian analytical psychology, encountered by the author through sustained reading and interpretation—particularly within the Japanese Jungian tradition articulated in the writings of Hayao Kawai. The core formative insight is structural: meaning and responsibility become possible only when experience is given form and boundary, rather than merely accumulating content. The evidential dialogue logs and symbolic diagrams included as case materials document the concrete moment where structurally deep inquiry externalizes previously unarticulated inner material into socially shareable and evaluable artifacts, motivating a structural parallel to individuation as boundary-making and communicable symbolization.
Finally, we must clarify the epistemic stance of this study. The objective of this paper is not to strictly "prove" a humanities concept using the standards of natural science. Rather, it is to present the structural conditions necessary to end a system where humanities-based judgments are dismissed as "arbitrary" or "mere impressions," allowing responsibility to evaporate.
By establishing operational definitions (Ghost Drift), structural boundaries (QSI/ATS), and non-retroactive evidence, we aim to construct a ground where accountability is structurally fixed. The "victory condition" of this research is not measurement itself, but the creation of a structure where the evasion of responsibility becomes impossible. Therefore, the pass/fail condition of this paper is defined strictly by whether the BP/RP artifacts are aligned and non-retroactively verifiable.
Ghost Drift can function under critical conditions, such as urgent, high-stress prompts. In these contexts, it enables AI to recursively restructure its responses based on user input, creating "Emergency Structural Intelligence." In emergencies, accountability usually evaporates. Ghost Drift ensures that even in a crisis, the AI produces outputs with clear assumptions and boundaries (High ATS), which is critical for decision support.
To activate Ghost Drift under crisis conditions, prompts must explicitly bind to a committed boundary and state stakes as machine-checkable fields.
In crisis settings, “sincerity” is not inferred. The criteria below are operational: they must appear as BP fields (objective/constraints/assumptions/checks/stakes) or as explicit references to prior commits.
Ghost Drift thus enables not just technical output but structural intelligence adaptable to emergency contexts, preserving the accountability boundary even when time is short.
Jung is not revived by quoting symbols or celebrating introspection. He is revived when meaning becomes a responsibility-bearing act. In AI-driven institutions, the dominant failure is not lack of intelligence but retroactive reinterpretation: criteria drift after outcomes, and harm occurs without assignable responsibility.
This paper presented a Jung-first accountability structure: commit a Boundary Packet (scope, assumptions, checks, PASS/FAIL or responsibility triggers), bind decision artifacts to that commit, and force every rebuttal to either (a) propose an alternative committed boundary and inherit responsibility, or (b) remain an unbounded objection that is logged but cannot dissolve the original responsibility surface.
Under this structure, “interpretation” stops functioning as a projection screen. It becomes owned meaning. That is the practical revival of Jung for the AI era: a psychology of meaning that cannot escape responsibility.
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