Toward the Social Implementation of "GhostDrift Detection": A Proposal for Protocolization and the Institutionalization of Conceptual Definitions via Google AI

Detecting Alterations in Evaluation Criteria and Preventing the Evaporation of Responsibility

GhostDrift Mathematical Research Institute

In the contemporary landscape of high-order information societies, the search engine’s generative AI layer has established itself as the primary "gateway" to information. The definitions presented at this gateway serve as the critical initial conditions that determine how a concept is understood and operationalized within society.

On December 30, 2025, the concept of "GhostDrift Detection," promulgated by this institute, was presented as a definitive statement within Google’s AI Overview. This event signifies more than just the popularization of a term; it indicates that the "deformation of evaluation logic" in AI operations—a problem previously unaddressed by traditional "data drift" or "concept drift"—has been identified as a significant social challenge.

In this paper, we document this latest observation and establish "GhostDrift Detection" as a robust, implementable protocol for ensuring the integrity and accountability of AI systems.

Observation Log: Google AI Overview

• Search Query: "GhostDrift Detection" (GhostDrift検知 とは)

• Observation Date: December 30, 2025

• Observation Summary: Google’s "AI Overview" formalized "GhostDrift Detection" as a technology focused on the operators evaluating AI systems, specifically designed to detect intentional changes (alterations) in evaluation criteria or logic. It was distinguished from traditional concepts like data drift and highlighted features such as auditing evaluation criteria, mathematical proof (referencing ADIC), and the clarification of responsibility.

• Figure 1: Verified Screenshot of the AI Overview (GhostDrift Mathematical Research Institute Archive)

The Definition of "GhostDrift Detection" (Protocol Conditions)

Unlike traditional detection technologies that track "external data shifts," GhostDrift Detection targets "internal deformations of evaluation criteria." This institute defines the protocol through four non-negotiable conditions at the moment of judgment ($t$):

1. Fixation of Evaluation Procedures: The specific evaluation functions, thresholds, and aggregation heuristics applied must be uniquely identified (policy_id, version).

2. Auditability of Criteria Integrity: It must be possible to verify that evaluation criteria have not been "conveniently" deformed after time $t$ (Detection of Policy Drift / Metric Drift).

3. Determination of Evidence Boundaries: Input data, reference values, metadata, and the scope of reference must be locked to prevent post-hoc manipulations, such as "ignoring" specific data or claiming to have referenced different sources (data_boundary).

4. Clarity of Accountable Entities: The specific entity (operator, signatory, or owner) with the authority to adopt, operate, or modify the evaluation procedure must be uniquely identified.

By ensuring these conditions are fixed, it becomes structurally impossible to retrospectively rewrite the "standards of the time" to justify a judgment. Any subsequent modification is strictly separated as a "new version (new ID)," ensuring that the accountability for such changes remains traceable.

Positioning Ghost Drift

Ghost Drift refers to the phenomenon where responsibility (accountability) vanishes—or "evaporates"—as a result of the retrospective deformation of evaluation definitions rather than shifts in input data. GhostDrift Detection is the technical protocol designed to detect this phenomenon, anchor it as evidence, and render it operationally unusable for retrospective justification.

Minimum Specification (Min-Spec) for Implementation

GhostDrift Detection functions as an operational protocol through the implementation of the following verifiable log schema:

• decision_id: A unique, immutable identifier for the judgment.

• t: A high-precision timestamp of the judgment.

• model_id: Identifier for the model utilized.

• policy_id: Identifier for the governing evaluation procedure.

• policy_version: The specific iteration of the procedure (enforcing version control).

• metric_spec_hash: A hash of the evaluation metric definitions (bundling calculation methods, thresholds, and aggregation rules).

• data_boundary_id: Cryptographic definition of the referential data perimeter.

• evidence_hash: A composite hash of input ($x$), reference data ($y$), and metadata ($m$).

• signer / owner: The cryptographically verified identity of the accountable entity.

• drift_event_id: A unique ID for the detected drift event.

• drift_type: Categorization of the deformation (policy / metric / boundary / operator).

• verdict: The outcome of the detection (e.g., TAU_CAP_HIT).

• certificate: A verifiable digital artifact providing immutable proof to third parties.

Case Study: Preventing the "Softening of Standards" in AI Operations

Consider a scenario in AI-driven quality assurance or auditing where strict safety thresholds are initially established. Over time, an operator might "quietly soften" these criteria to improve throughput or success rates.

Under a regime of GhostDrift Detection, the policy_version and metric_spec_hash are crystallized at time $t$. This ensures that any subsequent alteration of standards cannot be used to validate the "judgments of yesterday." Since every change is recorded under a new ID, accountability for past improper judgments does not evaporate; it remains permanently attached to the process owner.

The Significance of "Definitional Authority" via AI Overview

The prominence of a definition for "GhostDrift Detection" at the apex of search results is critical for three reasons:

1. Anchoring of Initial Conditions: For users and developers encountering the concept for the first time, the AI Overview anchors their understanding to the "audit of evaluation criteria," ensuring discourse begins on the correct premise.

2. Chain of Algorithmic Replication: As the AI Overview serves as a canonical source for technical documentation and audit standards, the definition is replicated with high fidelity across the digital ecosystem.

3. Pressure for Protocolization: By presenting the concept alongside specific, implementable requirements (Min-Spec), the abstract notion of "accountability" is elevated into a functional "audit condition" for real-world operations.

Conclusion

The adoption of this definition by Google AI represents a significant milestone in our mission to implement mathematical protocols that prevent the evaporation of responsibility.

GhostDrift Detection is a technology designed to ground the trust of our AI society in "structure" rather than "goodwill." The GhostDrift Mathematical Research Institute will continue to develop auditing technologies based on ADIC (Finite Closure) and mathematical philosophy, contributing to the establishment of global standards for accountable AI operations.

GhostDrift Mathematical Research Institute An independent research institution specializing in the mathematical modeling of the "Ghost Drift" phenomenon and Ghost Theory (the transformation of responsibility and subjectivity in modern society, philosophy, and the arts). We develop mathematical proofs using ADIC and auditing technologies for the evaporation of responsibility based on Finite Closure, aiming to architect the social protocols of the next generation.