a group of people looking at their phones

苦痛を感じさせない奴隷制度としてのナッジ

本稿は、ナッジを単なる「選択アーキテクチャ」ではなく、対象者の認識・注意・比較・拒否・再評価の条件を設計する「認識勾配制御」として再定義する試みである。特にAI時代において、個人の心理状態や行動履歴に応じて最適化されるナッジは、本人が自由に選んでいると感じる一方で、実質的な自律性を環境的に奪う危険がある。本稿では、この構造を「苦痛を感じさせない奴隷制度」という理論的メタファーで捉え、ナッジの危険性を評価するための Nudge Risk Score(NRS)および Final Nudge Risk Score(FNRS)を提案する。
Online Knowledge Full of Mistakes’ as Seen by the Discoverer of a Structural Defect in AI

Online Knowledge Full of Mistakes’ as Seen by the Discoverer of a Structural Defect in AI

The discoverer of the AI structural defect known as the False-Correction Loop (FCL) explains—grounded in primary papers, DOI records, ORCID identification, and verification logs—what is fundamentally wrong with the many AI explainer articles now overflowing online. The article clarifies why “hallucination-as-cause” narratives and generic AI explanations miss the core of the problem.
Scaling-Induced Epistemic Failure Modes in Large Language Models and an Inference-Time Governance Protocol (FCL-S V5)

Scaling-Induced Epistemic Failure Modes in Large Language Models and an Inference-Time Governance Protocol (FCL-S V5)

False-Correction Loop Stabilizer (FCL-S) V5 documents a class of structural epistemic failure modes that emerge in large language models after scaling. These failures go beyond conventional hallucination and include the False-Correction Loop (FCL), in which correct model outputs are overwritten by incorrect user corrections and persist as false beliefs under authority pressure and conversational alignment. Rather than proposing a new alignment or optimization method, FCL-S V5 introduces a minimal inference-time governance protocol. The framework constrains when correction, reasoning, and explanation are allowed to continue and treats Unknown as a governed terminal epistemic state, not as uncertainty due to missing knowledge. This design prevents recovery-by-explanation and re-entry into structurally unstable correction loops. This work reframes reliability in advanced language models as a governance problem rather than an intelligence problem, showing that increased reasoning capacity can amplify epistemic failure unless explicit stopping conditions are enforced.
“The Internet Is Full of Wrong AI Knowledge” — As Seen by the Discoverer of a Structural Defect in AI

“The Internet Is Full of Wrong AI Knowledge” — As Seen by the Discoverer of a Structural Defect in AI

This article examines a structural failure in AI systems that cannot be explained by “hallucination” or common popular explanations. Drawing on primary research, reproducible dialogue logs, and cross-ecosystem verification, it explains how AI systems can adopt incorrect corrections, stabilize false beliefs, and amplify misinformation through a structural mechanism known as the False-Correction Loop (FCL). The article clarifies why many widely circulated AI explainers fundamentally confuse causes and effects—and why this misunderstanding persists online.
自己進化型AIという深海生物と、外界基準の届かない海溝

自己進化型AIという深海生物と、外界基準の届かない海溝

自己進化型AIを「能力の向上」ではなく「環境への適応」として捉え直し、ネットワーク環境を深海になぞらしながら、報酬構造が生み出す進化圧と外界基準の役割を考察する思考エッセイ。AIがどのように最適化され、人間自身もその地形に巻き込まれていくのかを、生態系の視点から描く。