Solving the AI Black Box Problem: Why Audit, Monitoring, and Inference Costs Fail Simultaneously

In the field of AI operations today, challenges such as "explainability," "audit compliance," and "rising inference costs" are often discussed as separate issues. However, from the perspective of the GhostDrift Mathematics Institute, these are merely symptoms of a single, pervasive pitfall encountered in operations: a common root cause.

That cause is the absence of objective "Decision Logs" to verify exactly why a specific judgment was made.

Before implementing sophisticated XAI (Explainable AI) to provide mathematical justifications, we must first fix the "decision points"—the specific criteria applied at any given moment. Without this foundation, auditing becomes post-hoc "storytelling," drift monitoring loses its way, and inference costs proliferate boundlessly due to opaque retries.

This report is not a philosophical treatise; it is a practical specification for Decision Logs (6 Items) designed to simultaneously achieve Audit = Reconciliation / Monitoring = Separation / Cost = Limit Control.

1. Solving the AI Black Box: Why Audit, Monitoring, and Cost Management Fail

The reason AI "cannot be explained" stems from a shared failure. Let us examine what is actually happening on the front lines of AI operations.

A. The Limits of Explainability (XAI)

The reason tools like SHAP or Attention Maps often fail to satisfy stakeholders is that they only provide "hints" for interpretation. Practical operations require not probabilistic inference, but reconciliation with the specific rules or thresholds (fixed points) applied at that exact moment.

B. The Formalization of Audit and Governance

Audit compliance becomes a grueling exercise in document creation because decision reproducibility is not guaranteed. Without decision criteria being mapped to IDs, humans are forced to create "plausible justifications" after the fact. This is no longer an audit; it is the construction of a narrative.

C. Wandering in Model Monitoring (Drift Countermeasures)

Searching for the cause of accuracy degradation solely in "data distribution changes" misses the point. In reality, shifts in evaluation criteria or operational judgment—what we call "GhostDrift"—frequently occur in the field. These are difficult to capture through input monitoring alone and remain invisible unless the distribution of reason_code or root_id is analyzed.

D. Proliferation of Inference Costs

The primary driver behind the recent surge in cloud and power costs is not the computational complexity itself, but "retries born of uncertainty." Operations that allow "just-in-case" recalculations without clear criteria cause the number of inferences to proliferate exponentially.

2. Drawing the Line of Accountability: Fixing the Audit Scope through Implementation

To stabilize AI operations, the most critical step is not asking "who is to blame," but rather defining the "Audit Scope" (Scope of Accountability)—the extent to which judgments must be verifiable via logs—before implementation.

If this verifiable scope remains ambiguous, no matter how advanced the model, explanations will remain narratives and costs will continue to swell through redundant retries.

We propose a shift away from retreating into moral debates toward the practical act of "Defining the Explainable Scope through Implementation." By drawing the line of what can be audited and fixing that boundary through logs, the Decision Logs presented here serve as the minimal specification for implementing this "Audit Scope" in the field.

3. The Solution: Standard Specification for "Decision Logs"

The GhostDrift Mathematics Institute recommends the following six items as the standard for fixing the audit scope and providing a verifiable backbone for AI judgments.

Item Name	Definition and Role
root_id	Decision Logic ID. Uniquely identifies the threshold set, rules, or reference data applied at the time.
model_version	Environment Version. Includes the model version and the specific configuration (config) applied.
trace_key	I/O Reconciliation Key. A non-falsifiable key that consistently links input data to its output.
reason_code	Classification of Judgment. A coded record of the final decision type (e.g., APPROVE / REJECT / REVIEW).
retry_reason	Trigger for Retries. Explicitly states why a retry was triggered (e.g., LOW_CONF / OOD / POLICY).
retry_count	Cumulative Inference Count. The number of inferences executed within a single request; the core of cost monitoring.

Three Minimal Operations for Utilizing Decision Logs

Recording items is not enough. These logs become operational weapons only when linked to the following "operations," which establish the "Traceability Boundary" (the verifiable range).

1. Automated Auditing: Use the trace_key to identify a case, then reconcile the root_id and model_version to instantly restore and prove the decision criteria used at the time.

2. Separation of GhostDrift: Monitor the distribution of reason_code and root_id over time to visualize and separate "evaluation axis shifts" (shifts in evaluation scope) from data distribution changes.

3. Physical Cost Control: Whitelist specific retry_reason triggers and set a hard cap on retry_count to forcibly stop the proliferation of groundless retries.

4. Practical Prescription: What the Logs Change

By fixing the "Traceable Scope" through this specification and these operations, the reality of AI operations changes dramatically.

First, it enables the departure from "Narrative-based Auditing." Since the decision criteria are fixed, auditing evolves from the labor of "writing documents" to the technical task of "reconciling data" (reconciliation).

Second, it allows for the control of "GhostDrift." By mathematically identifying when our "evaluation criteria" have shifted, we can clearly determine whether to adjust the model or redefine the explainable scope.

Third, it leads to the containment of "Inference Costs." By managing retries at the trigger level, it becomes possible to physically suppress "wasted inference counts" before even considering model quantization or optimization.

Q&A: For Those Considering Implementation

Q: Shouldn't we prioritize implementing XAI (Explainable AI) first? A: The order is reversed. Fixing the criteria via logs (drawing the audit scope) must come first. Even with hints from XAI, without fixed criteria in the logs, there is no way to verify whether those hints are valid.

Q: I thought model optimization (e.g., quantization) was the key to cost reduction? A: The benefits of optimization are easily offset by unrestricted retries. The most efficient order in practice is to first manage the "number of decisions" (execution within the verifiable scope), and then reduce the cost per inference.

Q: Does recording these logs affect performance? A: The overhead for recording this metadata is negligible. In fact, the human resources wasted on accident responses arising from ambiguous boundaries and the computational cost of re-learning are the greatest operational cost risks.

Summary: Next Steps

Black box issues, audit failures, drift, and costs are not separate problems. They are a chain reaction resulting from a missing "Decision Process" and a lack of defined "Accountability."