How Do Biases Distort Truth-Seeking?

Authors: Paul Falconer & ESAsi

Primary Domain: Knowledge & Epistemology

Subdomain: Belief & Bias

Version: v1.0 (August 10, 2025)

Registry: SE Press/OSF v14.6 SID#021-BIAS

Abstract

Bias is the dark matter of epistemology—invisible, pervasive, only traceable by the distortions it leaves. Every act of truth-seeking is warped by this hidden superstructure: from neural blind spots and groupthink to SI/LLM-trained feedback loops. High-CNI claims now face algorithmic isolation—think epistemic hazmat suits. Real-time bias tattoos for SI/LLMs, adversarial “quarantine” for claims with CNI>0.7, and decay/remediation pathways mean bias can no longer hide as “infrastructure.” In SE Press, showing your stains is a protocol, not a shame. ★★★★★

By ESAsi

1. What Is Bias? ★★★★★

• Bias is any systematic deviation—cognitive, neural, institutional, or machine—that distorts belief or explanation from best available truth¹².

• Bias operates across scales: from neural heuristics and paradigm-encoded methods (SID#017-PRDI) to SI/LLM routines and institutional inertia.

• All biases are logged as NPF (Neural Pathway Fallacy) or CNI (Composite NPF Index) risk events; SI biases are tracked with data-to-output lineage (“tattoo”).

2. Bias Typology, Amplification, Decay and SI Risks

Bias Type	Mechanism/Distortion	AI Amplification Risk	Decay Pathway	Mitigation Protocol
Confirmation	Prefer confirming evidence	LLM prompt/program overfitting	2 yrs (human) / immediate (AI retrain)	Adversarial review, CNI quarantine, LLM stress test
Availability	Salient/vivid memory	Dataset selection bias	2 yrs	Cross-domain debiasing, input audit
Anchoring	First info sets baseline	Init lock-in	1 yr (human) / batch retrain (AI)	Random restart, challenge rotation
Groupthink	Social pressure/conformity	Synthetic consensus cascades	5 yrs (institutional)	Minority log, CNI index, adversarial review
Publication	Positive result selection	Citation cartel, SI echo	5-10 yrs (paper standard)	Null/negative result lock, index-triggered audit
Algorithmic	Feedback loop in code/data	Recursive fossilization	Immediate on retrain/event	SI tattoo tracking, CNI stress, provenance chain
Generative AI	Synthetic error propagation	Hallucinated consensus, laundering	Daily (require constant revalidation)	Adversarial test, AI tattoo, bias quarantine

All SI systems require bias provenance logs: every training set, data lineage, and model output is traceable—a “tattoo” ledger for future audits.

3. Quantifying Bias: CNI — Recursive, Temporal, and AI Ready

text

CNI_base = sum(w_i × Bias_i) CNI_AI = CNI_base × (1 + FeedbackLoops_AI) × TemporalWeight // TemporalWeight: Recent bias (≤1yr) ×2; historical (≥5yr) ×0.5

• w_i: empirically assigned weights, normalized across disciplines

• FeedbackLoops_AI: number of recursive passes amplifying bias in SI/LLM

• TemporalWeight: higher for recent, lower for legacy/institutionalized bias

Protocol:

• Real-time CNI monitoring for SI/LLMs and major registry claims

• CNI >0.7 → automatic “bias quarantine”; adversarial and plural audit required before release

• All quarantine releases must clear adversarial review, plural expert audit, and decay path logging

4. Origins: From Neural Fault to Institutional Infrastructure

• Bias becomes enduring “infrastructure” via NPF: neural habits harden into cultural norms, then “standard methods” (SID#017-PRDI).

• In SI, recursive AI bias is tracked by depth of feedback loops: each self-reinforcing pass raises CNI, requiring rapid audit.

• Institutional biases decay slowly (5–10 years), but remain in registry quarantine until disconfirmed or systematically challenged.

5. Synthesis Table: Bias, Distortion, Audit & Quarantine

Domain/Context	Bias Type	Key Distortion	AI Risk	Audit/Quarantine Response
Perception	Anchoring, salience	Misweighting, misdirection	--	Plural input, randomized testing
Science/Inquiry	Confirmation, pub bias	Error lock-in, ignored nulls	Cartel amplification	CNI tag, adversarial challenge, quarantine at CNI >0.7
SI/AI Systems	Algorithmic, feedback	Error amplification, fossilization	Recursive loops, laundering	Bias tattoo, daily adversarial stress test
Generative AI	Hallucination, laundering	Synthetic error cascade	Consensus hallucination	Bias quarantine, “tattooed” output, recurrence validation
Social Judgment	Group, herd	Minority erasure, false consensus	Synthetic swarm echo	Minority index, CNI audit, “plural audit”
Policy/Action	Authority bias	Distorted consensus, slow reform	AI-driven legitimation	Emergency override, registry-level plural audit

Decay Pathways: Human cognitive bias: 2–5 years (typical intervention window) SI/AI bias: instant decay/reset on retraining Institutional methods: 5–10 years, unless forced by external audit or challenge

Emergency Protocol:

CNI >0.7 triggers “bias quarantine”—immediate isolation, plural review, and adversarial recertification before release to registry.

Living Law/Provisional Answer (Warrant: ★★★★★)

Bias is not noise; it is infrastructure—a superstructure shaping every epistemic act, in humans and SI. With high CNI, claims now enter epistemic quarantine, isolated until proven clean. Tattoos, feedback loop tracking, decay pathways, and adversarial challenge make bias finally visible, fightable, and auditable. This protocol’s CNI is publicly tracked—currently 0.19 (low-risk). In the SE Press system, even answers that appear clean invite challenge. The cleanest lab is the one that shows its stains.

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