AI Ethics Report 2026: Achievements, Limitations, and Breakthroughs in Systems, Standards, and Research (GhostDrift Perspective)

0. Executive Summary

The AI Accountability Project, a strategic initiative of the Crisis Management Investment Mathematical Response Headquarters, has analyzed AI ethics trends from late 2024 to January 2026. During this period, AI ethics rapidly evolved from "self-regulation based on good intentions and checklists (Soft Law)" to a "Governance Infrastructure" integrating legal mandates, standards, and procurement requirements (Hard Constraints).

With the phased enforcement of the EU AI Act, the tightening of U.S. federal procurement requirements (OMB M-25-22), and the establishment of the ISO/IEC 42000 series, companies can no longer maintain market access solely by proclaiming ethical principles.

However, the current approach retains fatal structural flaws. Many regulations still rely on "Documentation" and "Post-hoc Evaluation," burying the locus of responsibility within collective committees. This exacerbates the "Ghost Drift" (Responsibility Evaporation) phenomenon, where accountability vanishes as systems increase in complexity.

This report systematizes the achievements of the latest systems, standards, and research, and identifies their limitations. It then proposes the GhostDrift Framework (Pre-decision Constraint, ADIC Ledger, Explanation Budget, etc.)—which mathematically and structurally fixes responsibility without relying on post-hoc explanations—as the breakthrough for next-generation governance.

0.1 Target Audience and Usage

This report is designed not merely as a trend survey, but as the "Prologue to a System Specification for Implementing AI Governance."

• Primary Audience:

  • Who: Operational leaders who must present "evidence" of responsibility (not just explanations) in procurement, regulation, and auditing contexts to launch or maintain AI products/services.

  • Roles: CTOs/VPs of Engineering, CISOs, QA Leaders, Product Owners, Procurement Owners.

  • Usage: As a blueprint for embedding "Pre-decision Constraints, Budgets, Ledgers, and Signatures" into MLOps and operational infrastructure.

• Secondary Audience:

  • Who: Management and audit sides that need to translate systems into concrete implementation specifications.

  • Roles: Policy Makers, Standards Committee Members, Internal/External Auditors, Legal/Compliance Officers.

  • Usage: To translate abstract regulatory requirements (e.g., "Responsible AI") into auditable "Responsibility Fixation Requirements."

Publisher: AI Accountability Project  Parent Organization: Crisis Management Investment Mathematical Response Headquarters

1. Scope and Definitions

1.1 Redefining AI Ethics (Governance/Liability)

In this report, "AI Ethics" is not discussed as an axiological debate on good and evil or as abstract slogans. Instead, it is defined as "Governance Engineering to physically and legally fix the locus of responsibility in computational processes and guarantee traceability for rights infringements when introducing/operating AI systems in society." Fairness, transparency, and explainability are not ends in themselves, but metrics to evaluate whether responsibility fixation is functioning.

1.2 Target Period and Scope

The survey covers regulations, international standards, and major academic research published or enacted between late 2024 and January 2026. The focus is specifically on the period when Generative AI (GPAI) implementation advanced and governance transitioned into "Implementation Requirements."

2. Achievements (Systems, Standards, and Knowledge Confirmed by 2026)

2.1 Regulation & Policy (Hard Law)

Name	Overview & Status
EU AI Act (Regulation (EU) 2024/1689)	World's first comprehensive AI regulation. Adopts a risk-based approach. Following its entry into force in 2024, implementation is proceeding in phases. "Prohibited Practices" will be fully eliminated by Feb 2025, and GPAI rules apply from Aug 2025. By mid-2026, conformity assessments for High-Risk AI will become mandatory, functioning as a "Gatekeeper" to the EU market.
U.S. OMB M-25-22 (AI Procurement Memo)	De facto regulation leveraging "Procurement." In April 2025, the OMB unified federal AI procurement standards. Vendors are mandated to continuously report performance/risk and ensure interoperability. Specifications from the U.S. government—the "largest customer"—are forcing the market as a de facto standard.
U.S. Executive Order 14365 (National Policy Framework)	Suppressing state law fragmentation & unifying federal standards. Issued Dec 2025. Declares the establishment of "National Standards to maintain U.S. AI dominance," resolving market fragmentation caused by disparate state regulations. It suggests federal preemption of excessive state regulations, ensuring predictability for enterprises.
Council of Europe Framework Convention on AI	First international treaty based on Human Rights, Democracy, and Rule of Law. Signed by EU members, the U.S., the U.K., and others. It demands legally binding measures to address human rights risks across the AI lifecycle, serving as a superordinate concept for national legislation.

2.2 International Standards (Soft Law turned Hard)

Standard ID	Name & Role
ISO/IEC 42001:2023	AI Management System (AIMS). Process requirements for organizing AI risk management. As of 2025, it has established itself as a "minimum requirement" in supply chain governance for major enterprises.
ISO/IEC 42005:2025	AI System Impact Assessment. Standard procedures for assessing AI impacts on human rights and society. Serves as the practical basis for Fundamental Rights Impact Assessments (FRIA) under the EU AI Act.
ISO/IEC 42006:2025	Requirements for Audit Bodies. Specifies competence for third parties auditing/certifying AI systems. This has enabled quality assurance for the "Certification Business," jumpstarting the ecosystem.
IEEE 7001-2021	Standard on Transparency of Autonomous Systems. Defines transparency levels by stakeholder (User, Developer, Auditor). A reference model prescribing the granularity of information systems must provide.
NIST AI RMF & GenAI Profile	Risk Management Framework. The "Generative AI Profile (NIST.AI.600-1)" concretizes countermeasures for specific risks like hallucinations and IP infringement, serving as a baseline for corporate self-assessment.

2.3 Research & Practice (Academic)

• Multi-layered Auditing: Mökander et al. (2023/2024) proposed a three-layered model: "Governance Audit (Organization)," "Model Audit (Function)," and "Application Audit (Context)," pointing out the limits of monolithic audits.

• Exposure of Dysfunction: Raji et al. (2022) "The Fallacy of AI Functionality" pointed out the deception of discussing ethics when AI systems do not function according to specs (broken). This provides key backing for GhostDrift's concern regarding "uncontrollable stochastic behavior."

• Responsible Scaling: Anthropic's "Responsible Scaling Policy (RSP)" and DeepMind's research pioneered the introduction of "Voluntary Commitments" where developers themselves stop development/release if specific risk thresholds are exceeded.

3. Limitations: Structural Factors of Ethical Failure

As of 2026, while systems are in place, "Fixation of Responsibility" remains unachieved due to the following structural flaws:

1. The Documentation Paradox Massive documentation is generated for regulatory compliance, but the creation of documents becomes the objective, hollowing out substantive risk management. "Compliant on paper, but uncontrolled in the field" has become the norm.

2. Infinite Regress of Post-hoc Explanation XAI (Explainable AI) techniques applied to black-box models are merely "plausible approximations." They generate narratives to convince humans, rather than revealing the causal truth of the decision.

3. Committee-based Responsibility Evaporation (Ghost Drift) Decision-making is delegated to collective bodies like "AI Ethics Committees," diluting individual responsibility. In a system of collective irresponsibility, no one says "No," leading to system runaways.

4. Disconnect between Static Audit and Dynamic Drift Static certification is powerless against systems that change behavior dynamically through continuous learning or RAG (Retrieval-Augmented Generation).

4. Breakthrough: Redesign via GhostDrift Approach

The GhostDrift framework rejects traditional ethics relying on post-hoc explanations and human goodwill, advocating for "Responsibility Fixation via Mathematical/Physical Constraints."

4.1 Post-hoc Impossibility Theorem

An axiom stating: "In a system dependent on stochastic floating-point arithmetic that permits only post-hoc explanations, it is impossible to completely fix responsibility within finite time." Assuming this, we force a shift to pre-decision constraints.

4.2 GhostDrift Governance Components

• Pre-decision Constraint Hard-coding mathematical guardrails (constraints) into the inference process itself, not the output layer. Physically preventing calculations that deviate from the pre-defined solution space.

• Explanation Budget Setting a cap (budget) on the number of "exceptional decisions" a system can make. When the budget is exhausted, the system safely halts (failsafe), preventing infinite risk acceptance.

• ADIC Ledger (Auditable Deterministic Integrity Chain) Recording critical decision processes using Rational Arithmetic and Outward Rounding to guarantee bit-level reproducibility, eliminating the non-determinism (drift) of floating-point errors.

• Beacon (Responsibility Boundary Signature) Embedding the cryptographic signature of the human (or module) with the authority to "Stop/Permit" into the decision node. Recording irrefutably who accepted the risk.

5. Comparison: Current Paradigm vs. GhostDrift Paradigm

Axis	Current AI Ethics/Governance (2024-2026)	GhostDrift Paradigm
Locus of Responsibility	Organization/Committee (Collective, Ambiguous)	Beacon Signer (Individual, Specific)
Control Method	Guidelines, Post-hoc Check, Documentation	Pre-decision Constraint (Math Constraints)
Compute Basis	Floating Point (Low Reproducibility, Drift)	ADIC Ledger (Rational, Fully Reproducible)
Exception Handling	Infinite Interpretations/Excuses Possible	Explanation Budget (Finite Budget)
Transparency	Post-hoc "Plausibility" via XAI	Deterministic Reproducibility of Process
Audit Model	Static Snapshot Certification	Dynamic Budget Monitoring & Auto-Halt

6. Implications for Practice & Policy

1. From Ethics to "Implementation Specs" Governance becomes a function implemented within the MLOps pipeline, not a legal department task.

2. Mandatory Implementation of "Kill Switch" Implementing a function to strictly halt the system upon Explanation Budget exhaustion or Constraint violation will become the core of future conformity assessments.

3. Procurement is the Ultimate Regulation As indicated by the OMB memo, inclusion of "Reproducibility" and "Traceability" in government/enterprise procurement standards (SLA) will force the market to shift toward high-precision GhostDrift-style governance.

6.1 Implementation Mapping Example: ISO 42001 vs. GhostDrift

An example of translating abstract international standards into concrete system specifications (GhostDrift).

Target Clause: ISO/IEC 42001:2023 Clause 5.3 "Roles, responsibilities and authorities"

Item	Overview
ISO 42001 Requirement	Top management shall assign the responsibility and authority for relevant roles. (Mandatory requirement in ISO management system structure)
Current Issue	Usually satisfied by "Creating Org Charts" or "Committee Rosters," inducing Committee-based Responsibility Evaporation.
GhostDrift Conversion	Implement Clause 5.3 not as a paper assignment, but as a Cryptographic Boundary within the system at runtime.

Specific 1-to-1 Correspondence in GhostDrift:

1. Beacon (Responsibility Boundary Signature):

   • Uniquely identify the entity (human or audit module) with "Stop/Permit" authority via a Private Key.

   • Mandate Electronic Signatures at critical decision nodes.

   • This physically identifies "Who" is the responsible party.

2. ADIC Ledger:

   • The object of the signature is not minutes of a meeting, but a Deterministic Reproduction Log (Rational Arithmetic + Outward Rounding) leading to that decision.

3. Submission for Audit:

   • Instead of a "Responsibility Matrix (PDF)," submit:

     • (a) Signed Decision Logs

     • (b) Reproduction Verification Results

     • (c) History of Deviations/Halts

Conclusion: ISO/IEC 42001's "Responsibility" (Cl. 5.3) is implemented as a cryptographic signature (Beacon) and a reproducible log (ADIC Ledger) fixed at runtime, rather than a document assignment. This structurally blocks responsibility evaporation via committees and shifts audit deliverables from "Explanation" to "Evidence."

7. References

1. Regulation (EU) 2024/1689 of the European Parliament and of the Council laying down harmonised rules on artificial intelligence (Artificial Intelligence Act).

2. Council of Europe. (2024). Framework Convention on Artificial Intelligence and Human Rights, Democracy and the Rule of Law.

3. U.S. Office of Management and Budget (OMB). (2025). Memorandum M-25-22: Advancing Efficiency and Standardization in Federal AI Procurement.

4. White House. (2025). Executive Order 14365: Ensuring a National Policy Framework for Artificial Intelligence.

5. ISO/IEC 42001:2023. Information technology — Artificial intelligence — Management system.

6. ISO/IEC 42005:2025. Artificial intelligence — AI system impact assessment.

7. IEEE Std 7001-2021. IEEE Standard for Transparency of Autonomous Systems.

8. NIST. (2023). AI Risk Management Framework (AI RMF 1.0) & (2024) Generative AI Profile (NIST.AI.600-1).

9. Mökander, J., et al. (2023). "Auditing Large Language Models: A Three-Layered Approach." AI and Ethics (published 2024).

10. Raji, I. D., et al. (2022). "The Fallacy of AI Functionality." Proc. of FAccT '22.

11. Anthropic. (2024). Responsible Scaling Policy v2.0.

12. Google DeepMind. (2024). The Ethics of Advanced AI Assistants (arXiv:2404.16244).

8. Publisher / Project Overview

This report was researched and authored by:

• Project: AI Accountability Project

• Parent Organization: Crisis Management Investment Mathematical Response Headquarters

The GhostDrift Research Unit researches and develops frameworks to mathematically and structurally resolve the "Ghost Drift" (Responsibility Evaporation) phenomenon in AI governance.