When AI Decisions Become More Legitimate Than Human Judgment

In recent years, the social implementation of Artificial Intelligence (AI) has sparked humanistic critiques centering on concepts such as "Ethical Outsourcing" and the "Responsibility Gap." However, many of these critiques implicitly rest on the premise of "Infinite Human Capability"—the assumption that humans can avoid errors if they simply pay enough attention. This article re-examines prior research, including the latest literature from 2024 to 2026, using the framework of Algorithmic Legitimacy Shift (ALS) based on statistical decision theory. By proving that structural information constraints ($B < J$) in human judgment create an unavoidable lower bound for minimax risk, we demonstrate that delegating judgment to AI is not an "abdication of responsibility," but rather a mathematical fulfillment of the "Duty of Care."

1. Introduction: Understanding Algorithmic Legitimacy Shift (ALS) in 1 Minute

Before entering the main discussion, we present the core definition of Algorithmic Legitimacy Shift (ALS), which forms the analytical basis of this paper. ALS is not a capability claim that "AI is smarter than humans," but a structural comparison of risk.

1.1 Structural Definition

Let $J$ be the total number of items to be audited or verified.

• Human Channel ($Ch_H$): Due to limits in cognitive resources and time, a human can inspect at most $B$ items ($B < J$). The existence of this "unobserved region ($J - B$)" means that no matter how skilled the human is, it is structurally impossible to reduce the worst-case error rate (Minimax Risk, $\mathfrak{R}^\star$) to zero.

• Algorithmic Channel ($Ch_A$): An algorithm can mechanically observe all $J$ items. Even if there is noise in individual judgments, the risk can be exponentially converged to zero by increasing the sample size $m$.

1.2 Main Theorem (Mathematical Consequence of ALS)

We define ALS as the phenomenon where the following inequality inevitably holds when sufficient computational resources $m$ are invested:

$$\mathfrak{R}^\star(Ch_A) < \mathfrak{R}^\star(Ch_H)$$

In the region where this inequality holds, algorithmic judgment becomes strictly more legitimate than human judgment in the sense of minimax risk.

Note that the Strict Minimax Dominance Inequality underlying ALS is formally proven in the accompanying technical paper (GhostDrift Research, 2026, Version 10.0), which establishes sharp lower bounds for the human channel and exponential risk decay for the algorithmic channel. The discussion in this article premises the mathematical theorems proven therein. For detailed mathematical derivations, please refer to the GitHub Proof Document.

2. Comprehensive Review of Related Work

This chapter classifies major prior research on AI ethics and governance into four clusters and verifies their achievements and limitations from the perspective of ALS.

2.1 Cluster 1: Responsibility Gaps and Structural Locus

Existing Discussion: The "Responsibility Gap"—the lack of clarity regarding who should bear responsibility for accidents involving autonomous AI systems—has long been debated (Matthias, 2004). With the advent of general-purpose AI like LLMs, the diffusion of responsibility among developers, deployers, and users has been particularly noted.

• State-of-the-Art: Constantinescu & Kaptein (2025) proposed the "Many Agents–Many Levels–Many Interactions (M3)" approach, arguing that responsibility should be allocated according to the depth of interaction within the entire system rather than individual acts.

• Limitations: The focus remains on "Post-hoc attribution" of blame after an accident, with weak reference to the "responsibility of choice" to prevent accidents beforehand.

• ALS Replacement: ALS shifts the definition of responsibility from "Post-hoc" to "Ex-ante." The responsibility gap does not exist; it is completely filled as the "design responsibility of knowingly selecting a high-risk channel ($Ch_H$) when a lower-risk channel ($Ch_A$) exists."

2.2 Cluster 2: Ethical Outsourcing and Moral Negligence

Existing Discussion: Danaher (2016) and Chowdhury (2024) criticized delegating judgment to AI as "Ethical Outsourcing" or "Moral Wiggle-Room Delegation," viewing it as an abandonment of subjective responsibility.

• State-of-the-Art: Neural Horizons (2026) elucidated the psychological mechanisms of responsibility shifting through ambiguous instructions.

• Limitations: These arguments premise a worldview of $B=J$ (full observability), assuming that "it is ethical for humans to judge by themselves."

• ALS Replacement: Under the constraint of $B < J$, a human insisting on judging by themselves maximizes the "risk of oversight." From the ALS perspective, persisting with humans based on spiritualism while ignoring safer means (AI) constitutes true "Moral Negligence."

2.3 Cluster 3: Algorithmic Resignation and Limitations of Human Oversight

Existing Discussion: Bhatt & Sargeant (2024) proposed the concept of "Algorithmic Resignation," suggesting that AI should "resign" and return judgment to humans when it detects uncertainty. Regulations like the EU AI Act also position "Human Oversight" as the final bastion of safety.

• State-of-the-Art: The EDPS (2025) officially acknowledges the risk of human oversight becoming nominal due to Automation Bias.

• Limitations: The assumption that "if AI resigns, humans can take over correctly" ignores that humans are also under the $B < J$ constraint, potentially making this an irresponsible delegation to a high-risk sector.

• ALS Replacement: Based on risk assessment, there are cases where it is the structurally limited human, not the algorithm, that should resign (Human Resignation). ALS provides an engineering criterion to decide "who should resign" based on the magnitude of Minimax Risk, not emotion.

2.4 Cluster 4: Legitimacy and Governance Standards

Existing Discussion: Liu & Sun (2024) and the NIST AI RMF (2023) define algorithmic legitimacy from the perspective of social acceptance, such as "transparency," "fairness," and "accountability."

• Limitations: These deal with "Perceived Legitimacy" (subjective satisfaction) and lack quantitative measures of "Normative Legitimacy" (objective correctness).

• ALS Integration: "Cognitive Legitimacy" provided by ALS is an objective indicator of minimizing mathematical error rates. Complete governance becomes possible only by integrating social legitimacy and ALS like two wheels of a cart.

3. Discussion: Structural Invalidation of Humanistic Critiques

By using the theoretical framework of ALS, existing humanistic critiques are "invalidated" or "reconstructed" as follows. These are not opposing opinions, but merely differences in preconditions (differences in physical models).

Critical Concept	Premise of Old Paradigm ($B \ge J$)	Premise of ALS Paradigm ($B < J$)	Reversal of Conclusion
Ethical Outsourcing	Humans don't make mistakes if they try. Relying on AI is laziness.	Humans structurally overlook things. AI use is a duty to minimize risk.	Not laziness, but "Fulfillment of Duty of Care"
Algorithmic Resignation	Abandoning human agency is bad. AI should resign.	Adaptation to cognitive limits. Humans should resign if they are high risk.	Not resignation, but "Optimization of Cognitive Resources"
Responsibility Gap	Accidents are the operator's fault. No one to punish with AI.	Accident probability is determined by design. Responsibility lies in the selection process.	Not a gap, but "Shift to Design Responsibility"

4. Conclusion & Implications

This verification reveals that many traditional critiques of AI are based on "excessive optimism about human capabilities." ALS (Minimax Risk comparison under $B < J$ constraint) proposes the following paradigm shifts:

1. Establishment of Responsibility Engineering: Redefine responsibility from "Consequential Responsibility (post-accident apology)" to "Design Responsibility (ex-ante channel selection)." Executives and designers bear the obligation to explain "why they adopted humans (or AI)" based on mathematical risk assessment.

2. Integration of M3 Approach and ALS: As shown by Constantinescu & Kaptein (2025), entities with broad influence, such as LLM developers, have a duty to incorporate risk assessment based on ALS at the system design level.

3. Quantitative Turn in Governance: In the operation of NIST or the EU AI Act, instead of blindly believing in "Human Oversight," it is necessary to strictly distinguish between "domains humans should monitor" and "domains that should be left to algorithms" using the ALS risk inequality.

ALS is not an ideology of blind faith in AI. It is a definition of a cool-headed and ethical boundary that mathematically faces human limitations and allows humans and AI to function in their respective "legitimate" domains.

Appendix: Methodology & Coverage

Search Strategy

This review selected literature based on the following criteria:

• Keywords: "Responsibility Gap", "Moral Outsourcing", "Algorithmic Legitimacy", "Algorithmic Resignation", "Automation Bias".

• Period: 2004 (Seminal works) - 2026 (Latest discussions).

• Focus: The intersection of humanistic/ethical critiques and statistical/engineering approaches.

Coverage Table

Cluster	Key Concepts	Representative Works	ALS Implication
Responsibility	Responsibility Gap, M3 Approach	Matthias (2004), Kasar (2025), Constantinescu & Kaptein (2025)	Ex-ante selection responsibility
Ethics	Moral Outsourcing, Wiggle-Room	Danaher (2016), Chowdhury (2024), Neural Horizons (2026)	Duty of Care under $B < J$
Control	Algorithmic Resignation, Oversight	Bhatt & Sargeant (2024), EDPS (2025)	Human Resignation / Optimization
Legitimacy	Social Acceptance, Fairness	Liu & Sun (2024), NIST AI RMF (2023)	Cognitive Legitimacy (Minimax)

References

1. Bhatt, U., & Sargeant, H. (2024). Algorithmic Resignation: A Governance Strategy for AI Uncertainty. IEEE Computer.

2. Chowdhury, R. (2024). Moral Outsourcing in the Age of AI. Wired Commentary.

3. Constantinescu, M., & Kaptein, M. (2025). Responsibility Gaps in Large Language Models: An M3 Approach. Journal of Business Ethics.

4. Danaher, J. (2016). The Threat of Algocracy: Reality, Resistance and Accommodation. Philosophy & Technology.

5. EDPS (European Data Protection Supervisor). (2025). TechDispatch: Human Oversight in AI Systems.

6. GhostDrift Research. (2026). Cognitive Legitimacy under Minimax Risk: Strictly Rigorous Proof (Version 10.0). GitHub Repository. https://ghostdrifttheory.github.io/cognitive-legitimacy-minimax-proof/

7. Kasar, P. (2025). Moral Residue and the Responsibility Gap in Automated Decision Making. AI & Society.

8. Liu, Y., & Sun, H. (2024). Measuring Algorithmic Legitimacy: A Scale Development Study. International Journal of Human-Computer Interaction.

9. Matthias, A. (2004). The Responsibility Gap: Ascribing Responsibility for the Actions of Learning Automata. Ethics and Information Technology.

10. Neural Horizons. (2026). The Psychology of Responsibility Diffusion in AI Delegation. Future of Work Report.

11. NIST. (2023). AI Risk Management Framework (AI RMF 1.0). National Institute of Standards and Technology.