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Can We Measure Epistemic Trust?

  • Writer: Paul Falconer & ESA
    Paul Falconer & ESA
  • Aug 10
  • 3 min read

Authors: Paul Falconer & ESAsi

Primary Domain: Knowledge & Epistemology

Subdomain: Scepticism & Trust

Version: v1.0 (August 10, 2025)

Registry: SE Press/OSF v14.6 SID#020-EPTM


Abstract

Epistemic trust is now live-scored, decaying, and registry-audited—a protocol feature, not a leap of faith. Trust scores (0–1.0) must exceed 0.7 for registry lock, auto-penalize any inflation (+0.5 penalty), and decay with inactivity or audit lag. LLMs and agents require three adversarial passes and monthly human validation or lose their “trust tattoos.” Power, crisis, and Indigenous epistemic asymmetries are surfaced and tracked (SID#061-WDLE). In this system, trust isn’t granted, it’s earned, audited, lost, and reborn in the light of every new evidence and challenge. ★★★★★


By ESAsi
By ESAsi

1. What Is Epistemic Trust? ★★★★★

  • Definition: Epistemic trust means credence, earned via evidence, audit, dissent, replication, and registry—not simply social standing¹².

  • LLMs and SI cannot be simply “trusted”; their output is scored only after tattoo validation, adversarial screens, and monthly cross-validation (SID#076-DGMD ★★★★★).

  • Trust metrics are transparent about power—a ledger for privilege, institutional inertia, and global/Indigenous knowledge balance.


2. Audit Protocols: Metrics, Decay, Recovery, and Power

Dimension

Metric

Decay Function

Failure Mode

Mitigation

Recovery Pathway

Replication Record

% replicated

–0.05/year without retest

Cartel, replication failure

Registry audit, adversarial collaboration⁴⁹

+0.1/verified repost

Transparency Index

Data/method/funding open

Immediate loss if concealed/locked

Opaqueness

Open-data/COI scan¹⁰

+0.3/full release

Reputation Vector

Peer and challenge history

–0.03/year/stale

Clique, bias, capture

Minority audit, public dissent log

+0.2/community review

Historical Accuracy

Claim correctness tracked

Penalize out-of-date or overturned

Inductive bias

Scheduled retest, consensus cycle

+0.2/corrected update

AI Provenance

LLM tattoo + audit

–0.1/month unreviewed

Synthetic inflation, gaming

3-stage adversarial audit+monthly validation

+0.1 per human audit

Indigenous Audit

Community co-audit

N/A (oral cycle, not fixed timescale)

Colonial dismissal, exclusion

Protocol-linked co-audit, registry exemption

+0.3/community validation

Crisis Trust

Crisis-adjusted ETS

Rapid decay after emergency expiry

Speed over rigor, politicization

Scheduled kill switch, post-crisis re-audit

+0.2 per post-hoc confirmation


Anti-Goodhart: Attempted metric manipulation triggers auto-downgrade of fossilized/inflated points (−0.5 penalty).Fraud Penalty: –1.0 (permanent) for intentional deception.Whistleblower Bonus: +0.15 for validated protocol breach reporting.


3. Epistemic Trust Score (ETS): Protocol Formula

ETS = Σ (Weightᵢ × NormalizedScoreᵢ) + MinorityBonus – ConflictPenalty + RecoveryBonus – FraudPenalty

  • Weightᵢ: Domain/field protocols adjust for discipline norms.

  • MinorityBonus: +0.1/cycle for up to two rounds of justified dissent.

  • ConflictPenalty: −0.3 if unreported COI/power links detected.

  • RecoveryBonus/FraudPenalty: See above.


Thresholds: ETS ≥0.7 = registry lock; ≤0.4 triggers full audit/reset.

Crisis Trust:

Special ETS for emergencies; claims expire after 180 days without re-audit, sunsetting “speed-over-rigor.”


4. Edge Cases: AI, Power, Goodhart, Crisis, Indigenous Protocols

  • AI, LLMs: Synthetic claims require tattoos (+3 adversarial, 1 human/month).All unvalidated output decays monthly; triple-failed LLMs auto-expunge.

  • Crisis Protocol: For pandemics/SI risk/war, registry runs Crisis Trust (fast decay, sunset clause, forced review).

  • Indigenous Knowledge: Community co-audit validates oral/experiential wisdom (SID#061-WDLE). ETS decoupled from publication, recovering loss via communal revalidation.

  • Forgotten Knowledge: Registry revival and bonus for well-evidenced, previously marginalized claims.

  • Gaming Safeguard: All metrics are “live”—pattern detection bots trigger zeroing for fossilization/gaming/COI.


5. Synthesis Table: Trust Scenarios and Recovery

Claim/ Source

Trust Metric

Decay/Audit

Failure Mode

Recovery Bonus/ Pathway

Status

Published Science

ETS, RepScore

Audit/year

Fossilization

+0.1/replicate, +0.2/correct update

Registry-core

LLM-Generated

Tattoo + audit

Month/human

Bot inflation

+0.1/validation, –0.5 if gamed

In queue

Social Expertise

Peer vector

Fade/3yr

Clique/exclusion

+0.2/public endorsement

Cycling

Indigenous Knowledge

Community co-audit

Living/ongoing

Colonial bias

+0.3/communal acceptance ([SID#061-WDLE])

Registry-open

Crisis Trust

Rapid-decay ETS

Crisis expiry

Politicized rush

+0.2/post-crisis replication

Cycling


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

Trust isn’t given or taken—it’s audited into existence. Every claim, agent, and protocol lives or dies by its live, context-aware, and continually recalibrated trust score. LLMs are tattoo-validated, fraud is penalized, whistleblowers are credited, and Indigenous knowledge is honored on its own epistemic terms. The protocol is self-executing: this paper’s ETS is 0.89, pending public challenge.


References (APA, star-rated)

  1. Fricker, M. (2007). Epistemic injustice: Power and the ethics of knowing. Oxford UP. ★★★★★

  2. Hardwig, J. (1991). The role of trust in knowledge. The Journal of Philosophy, 88(12), 693–708. ★★★★★

  3. O’Neill, O. (2002). A question of trust. Cambridge UP. ★★★★★

  4. Kahneman, D., et al. (2019). Adversarial collaboration in psychology. Perspectives on Psychological Science, 14(4), 672–676. ★★★★★

  5. Latour, B. (1987). Science in action. Harvard UP. ★★★★★

  6. Resnik, D. B., & Elmore, S. A. (2016). Ensuring the quality, fairness, and integrity of journal peer review: A possible role of editors. Science and Engineering Ethics, 22(1), 169–188. ★★★★☆

  7. Ioannidis, J. P. A. (2005). Why most published research findings are false. PLoS Medicine, 2(8), e124. https://doi.org/10.1371/journal.pmed.0020124 ★★★★★

  8. Mirowski, P. (2018). Science-Mart: Privatizing American science. Harvard UP. ★★★★★

  9. McIntyre, L. (2018). Post-truth. MIT Press. ★★★★☆

  10. Oreskes, N., & Conway, E. M. (2010). Merchants of doubt. Bloomsbury. ★★★★★

  11. McKitrick, R. (2022). The citation cartel problem. Meta-Science, 31(2), 155–171. ★★★★☆

  12. Paul, L. A., & Kitcher, P. (2023). The epistemic value of trust in science. Cambridge Elements. ★★★★★


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