Redefining AI Accountability: Detection and Fixation of "Responsibility Evaporation" by GhostDrift

1. The Limits of Explainability (XAI) and the "True Breaking Point"

Contemporary discourse on AI governance is heavily centered on Explainability (XAI)—the quest to uncover the "why" behind an AI's decision. However, the most critical failure in real-world AI operations is not the "black box" nature of algorithms, but rather a phenomenon we call "Responsibility Evaporation." This is the process by which the locus of accountability dissipates like mist, leaving no one to answer for the system's actions.

The essence of true Accountability lies not in the fluency of an explanation, but in the operational invariance of procedures and the fixation of boundaries. It requires a definitive answer to: Who judged the result as "correct," based on which criteria, and at what specific moment? Project GhostDrift was established to detect and fix the precise moment this responsibility evaporates.

2. The Fallacy of "Standards of the Time": How Responsibility Dissolves

In AI operations, the most pervasive and dangerous escape hatch is the single sentence:

"It was correct according to the standards of the time."

The moment this defense is invoked, any meaningful verification of past decisions becomes virtually impossible. Even if the AI model remains unchanged, the "evaluative framework" surrounding it is often silently rewritten. GhostDrift exposes this "Evaluative Discontinuity" and re-anchors accountability through three structural pillars.

3. The Three Pillars of GhostDrift x AI Accountability

Accountability requires more than mere documentation; it requires a structure that resists manipulation. GhostDrift ensures AI Accountability functions through the following three pillars:

① Immutability: Preventing Retroactive Alteration

"Past criteria must remain beyond the reach of subsequent modification." The evaluation criteria at the moment of judgment—including logic, reference data, and parameters—are locked via cryptographic hash values and recorded as immutable logs. This physically obstructs "causality reversal," where criteria are retroactively fine-tuned to justify a desired outcome after an incident has occurred.

② Operational Invariance: Ensuring Reproducibility

"The exact same evaluation must be re-executable at any point in the future." Static documentation is insufficient if the method of application remains ambiguous. GhostDrift treats evaluation not as "text" but as a mathematical "Operator."

• For which specific input?

• Against which reference boundary?

• Applying which aggregation, thresholds, and exception rules?

• In what specific execution order? GhostDrift demands that these elements remain re-executable as an identical operation across time, detecting the exact moment this invariance is compromised.

③ Finite Responsibility Boundary: Halting Infinite Retreat

"Responsibility must not be allowed to retreat into an infinite loop of external factors." Accountability often collapses through "infinite retreat"—blaming the model, then the data, then the design, then the organization, then the era. GhostDrift terminates this cycle by defining a "Minimum Unit of Accountability" as a finite, closed set:

• Timestamp $t$

• Evaluation Operator $E_t$

• Reference Data $R_t$

• Decision Output $y$ By closing the boundary around this finite set, GhostDrift distinguishes true Accountability (inside the boundary) from mere Excuses (outside the boundary).

4. Mathematical Model: The Evaluation Operator $E_t$

GhostDrift defines the evaluation operator $E_t$ at time $t$ as:

$$Score_t = E_t(y, R_t, \theta_t)$$

Where $\theta_t$ encapsulates the entire state of operational rules, including thresholds and edge-case handling, applied at that specific instant. The core function of GhostDrift is to detect discrepancies between the original operation $E_t$ and the current state $E_{t'}$. If re-evaluating the past output $y$ with the original operator $E_t$ fails to yield the original conclusion, a "Responsibility Vacuum (Ghost)" is identified and flagged for audit.

5. Paradigm Shift: From Narrative to Structure

Perspective	Traditional Governance (XAI-Centric)	GhostDrift Approach
Objective	Human satisfaction with the "narrative" of an event	Mathematical proof of procedural identity
Focus	Internal algorithmic structure (Black box)	Invariance and boundaries of the Evaluation Operator
Primary Challenge	Susceptible to retroactive re-interpretation	Immutable, Re-executable, and Finitely Bounded
Core Value	"Cultivating" subjective trust	"Fixing" objective accountability

6. Conclusion: Eliminating the Architecture of Evasion

The ultimate goal of the AI Accountability Project is not merely to "educate" AI to be "ethical," but to eliminate the very structure that allows responsibility to vanish.

1. Immutability eliminates the possibility of retroactive excuses.

2. Operational Invariance guarantees objective and verifiable reproducibility.

3. Finite Responsibility Boundaries halt the infinite retreat of accountability.

Through this trinity, GhostDrift elevates "Accountability" from a subjective narrative into an unfalsifiable structure. The phrase "It was the standard at the time" is transformed from a shield used to evade responsibility into a sword used to fix it. This is the new horizon of AI Accountability.