How is GhostDrift Understood? Record of its Positioning as a "Theoretical Framework" in Google AI Overview

Date: December 2025

Observation Target: Google Search results (AI Overview) for the query "Trusted AI GhostDrift"

Recorder: GhostDrift Mathematical Institute (GMI)

0. Overview

This report is a record of the observation that the term "GhostDrift" has been summarized and structured in Google’s AI Overview as "not a specific product name, but a research and theoretical framework concerning AI safety, reliability, and accountability."

The purpose of this record is not to claim specific achievements, but to preserve a "observation log" of how the search AI organized and presented this concept, serving as a "trust buffer" for future theoretical development and social implementation.

1. Observation Conditions

• Timestamp: December 2025 (JST)

• Search Query: Trusted AI GhostDrift

• Target UI: Google Search (AI Overview display)

• Region / Language: Japan / Japanese UI

• Device: Desktop

• Browser: Chrome-based

• Login Status: Based on the state at the time the screenshot was taken (Logged in)

2. Observation Data: What the AI Overview Presented

The AI Overview summary positioned GhostDrift as follows:

2.1 Key Summarized Points

1. Nature of the Term: GhostDrift is recognized not as a specific "Trusted AI product/service name," but as a broad research and theoretical framework.

2. Domain: Described primarily as a research framework for "AI Safety," "Reliability," and "Accountability."

3. Proposing Entity: It is noted that the GhostDrift Mathematical Institute (GMI) proposed this concept and is conducting a mathematical approach.

4. Problem Setting: Described in the context of mathematically handling accountability and evaluation methods when AI exhibits "unpredictable behavior (drift)."

2.2 Characteristic Descriptions (Extracted from Image)

The AI Overview includes the following types of descriptions for GhostDrift:

• Theory and Research: It addresses issues accompanying environmental changes (domain shifts) or data conditions (biases, etc.) from the perspectives of accountability and evaluation.

• Approach to Reliability: Rather than a probabilistic "probably correct" inference, the approach is categorized as moving toward verifiable forms (verifiability/reproducibility).

• Not a Commercial Product: It is explicitly stated that there is no confirmation of commercial services directly available to general users at this time, identifying it as a concept in the research and development stage.

3. Conceptual Reflection: Significance of this Positioning

Note: The following is not the observation result itself, but an interpretation and organization by GMI.

This observation demonstrates that the search AI organizes "GhostDrift" not as a mere buzzword or product name, but as a high-level theoretical framework for handling AI governance (Safety, Reliability, and Accountability).

What is crucial here is that the "Problem (Phenomenon)" and the "Theory (Framework)" are starting to be understood as separate entities even within search algorithms:

• "AI Accountability Ghost": The phenomenon where responsibility becomes ambiguous (evaporates). A conceptual node on the societal problem side.

• "GhostDrift": The theoretical framework for handling that phenomenon. A conceptual node on the mathematical/audit design side.

This observation shows that the organization of the latter (the theoretical framework) has been established objectively by the AI.

4. GMI's Definition: Minimum Definition as a Theoretical Framework

Note: The following is not the observation result itself, but an interpretation and organization by GMI.

As this report is an observation log, GMI's assertions are limited here to a minimum "definition" level.

• GhostDrift: A theoretical framework for formalizing factors that make it difficult to fix evaluation and accountability in operations (shifts in evaluation criteria, alteration of evaluations, changes in operational conditions, etc.) into an auditable format.

• Objective: To enable the design and verification of boundary conditions (non-retroactive fixation, identity of evaluation operators, clarification of responsibility boundaries) necessary for Accountability Assignment, rather than mere Explainability.

5. Positioning of this Record: An "Observation Log" rather than an Achievement

Note: The following is not the observation result itself, but an interpretation and organization by GMI.

This report is not intended to boast of specific results. It is a record to preserve as an "observation log" the fact that the search AI bundled "GhostDrift" as a theoretical framework encompassing the three pillars of:

1. Safety

2. Reliability

3. Accountability

6. Related Resources

Algorithms

• Detecting Evaluation Schema Shifts in AI Monitoring: A Mathematical Framework with GhostDrift Algorithm

Responsibility Boundaries and the Zeta Function

• Why the Zeta Function is Necessary for AI Safety: The Mathematics That Separates Cause and Effect and Defines Responsibility Boundaries

New Metric for AI Safety | ADIC

• A Paradigm Shift in AI Safety: Why ADIC Reframes Mathematical Models as Accountable Tools

© 2025 GhostDrift Mathematical Institute (GMI)

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