[Project Overview] AI Accountability Project: Responsibility Evaporates in Explanations ── Mathematical Evidence Guarded by Post-hoc Impossibility

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1月2日
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Responsibility vanishes with the phrase: It was correct at the time.Accountability evaporates when verification is absent. ▶ AI Accountability Project Official Page

Preface: After an Accident, Responsibility Dissipates into Post-hoc Words

In an era where AI governs critical social infrastructure, the most severe problem is not the accident itself, but the Evaporation of Responsibility that occurs immediately afterward.

Statements like: The criteria were optimal at the time or The data deviated from our assumptions are common. While they appear to be explanations, they are essentially Post-hoc Explanations used to avoid responsibility.

The AI Accountability Project is designed to stop this evaporation. Our weapon is not a convincing narrative, but mathematical evidence that ensures Post-hoc Impossibility.

1. Core Concept: Post-hoc Impossibility ── Physically Blocking Moving the Goalposts

The highest-order concept of this project is Post-hoc Impossibility.

Most AI safety research focuses on making the internal workings of a model more explainable. However, the more flexible the explanation, the easier it is to rewrite the interpretation to suit one's needs after an accident.

We physically fix the premises of judgment, execution logic, evaluation criteria, and input data integrity at the exact moment a decision is made. This makes it systematically impossible to move the goalposts or engage in a post-hoc game of moving the goalposts after an event occurs.

2. Philosophy: Fixing Responsibility through Verification, Not Explanation

We do not question the psychology of the AI (what it thought). Instead, we use a physical benchmark: can a third party reach the same conclusion using the same data?

Fixation of Input: What exactly was viewed to make the judgment?
Fixation of Logic: Which mathematical model was used and how did it operate?
Fixation of Policy: What criteria were used to determine a pass?

Only when these are determined before an accident and cannot be changed after (Post-hoc Impossibility) does responsibility remain firmly in place.

3. Definition: What We Mean by Accountability

In this project, AI Accountability is defined as a systemic structure that satisfies the following five conditions.

Audit Indicators: The 5 Requirements for Verifiability

Data Binding (I/O Fixation): Mathematical proof of the integrity of input data via hash values.
Reproducibility: Ability for a third party to re-execute and achieve the identical result using only the provided Artifact Bundle.
Tamper-resistance: Logs protected at the system level, making post-hoc alteration impossible.
Fixed Verdict: PASS/FAIL criteria defined in advance that cannot be modified post-hoc.
Post-hoc Impossibility: The synthesis of the above, ensuring that subsequent justifications cannot overturn the verdict.

4. ADIC: A Public Audit Protocol for Verification via PASS/FAIL

Our core protocol, ADIC (Audit of Drift in Context), intentionally ignores plausible internal explanations from the AI. Instead, it asks: Is this judgment verifiable based on the agreement at the time? and generates an Artifact Bundle (bundle of evidence).

[Case Study: Audit of Structural Change in Electricity Demand Forecasting] We present and publicly release the following Artifact Bundle as a comprehensive package:

▶ Public Audit Report: ADIC Verification Results for Electricity Demand Forecasting

Audit Report (PDF): Mathematical analysis of structural changes and grounds for the verdict.
Audit Log (JSONL): Full records of parameters and judgment processes during execution.
Reproducible Code (Python/Repo): Scripts for a third party to set up the environment and verify the results.
Data Integrity Evidence (SHA-256): Hash values of the audited data.
Verdict: NG (Identified the invisibility of Ghost Events caused by budget policy alert suppression).

Demonstrating Post-hoc Impossibility: The subjective defense that We thought it was safe at the time is invalidated by the ADIC audit logs, which prove the mathematical failure at that moment.

5. Why Existing AI Safety Measures Continue to Fail

Existing governance fails to achieve true accountability because it confuses Explainability with Accountability.

The Black Box Trap: Attempting to explain complex models increases verification costs, leading stakeholders to abandon verification in favor of unverified expectations.
The Limits of Log Faith: Records alone are insufficient. Without an evidentiary structure, logs are absorbed into post-hoc interpretative battles and become noise.
Moving Goalposts: As long as criteria are dynamic, responsibility will continue to evaporate.

6. Redefining Terms: Weapons for Practical Implementation

Transparency: Not about seeing inside the model, but about the judgment process being Reproducible.
Privacy: Not about hiding data, but the coexistence of minimal disclosure and Verifiability.
Post-hoc Impossibility: Stripping away explanatory words and replacing them with the Fixation of Evidence.

7. Specialized Series: Dissecting the Moment Real-world Practice Stops

Mathematical reasons why AI risk management fails to solve accountability.
The minimum checklist for preventing the evaporation of responsibility during governance construction.
ADIC implementation: Retaining responsibility even during security breaches.
The absolute value of Post-hoc Impossibility in healthcare, autonomous driving, and finance.

Conclusion: Do Not Increase Explanations; Fix the Evidence

Responsibility vanishes after an accident not because of a lack of ethics, but because post-hoc impossible evidence was never prepared from the start.

The AI Accountability Project silences the AI's narratives and uses mathematics to structurally fix its responsibility.

▶ AI Accountability Project: Project Details and Audit Reports

Contact

GhostDrift Research (Mathematical Verification Lab) supports responsible decision-making and the implementation of governance.

AI Security/Governance: Implementation of verifiability that withstands regulation and auditing.
Corporate PoC: Demonstrations for converting operational risks into verifiable evidence.
Mathematical Model Research: Research on next-generation architectures based on Finite Closure and GhostDrift Theory.