Global Epistemic Threats: Risks and Mitigations

• Global Epistemic Threats are risks that erode society’s ability to maintain shared factual reference points and reliable knowledge transmission.
• GETs manifest across disciplines such as generative AI, turbulence modeling, and digital media through algorithmic bias and synthetic realism.
• Mitigation strategies include robust provenance, AI forensic tools, culturally specific modeling, and governance frameworks to preserve epistemic integrity.

Global Epistemic Threats (GETs) are risks that, when realized at scale, undermine a society’s or humanity’s ability to maintain shared reference points, factual concordance, or reliable pathways for knowledge transmission. Distinguished by their systemic reach and their ability to atomize or erode the infrastructure of trust and epistemic warrant, GETs manifest across multiple disciplines—including the governance of LLMs, turbulence modeling in fluid dynamics, and global information ecosystems shaped by generative artificial intelligence. This article synthesizes the core conceptualizations, formal frameworks, empirical analyses, and mitigation strategies from recent research, with a focus on the epistemic consequences of generative AI and related phenomena.

1. Definitional Landscape of Global Epistemic Threats

A Global Epistemic Threat is any phenomenon that, at societal or global scale, contaminates, fragments, or otherwise vitiates the epistemic environment necessary for producing, assessing, and trusting knowledge. GETs are distinct from localized or interpersonal epistemic harms because they operate structurally, sweeping across languages, nations, communities, or scientific/institutional domains. GETs erode foundational epistemic practices such as evidence-gathering, source-criticism, justificatory reflection, and communal knowledge production (Ferrara, 12 Nov 2024, Hila, 22 Dec 2025, Kay et al., 21 Aug 2024).

In the context of generative AI, GETs include the proliferation of personalized synthetic realities: algorithmically generated media and narratives tailored to individual preferences, which distort objective facts and reinforce preexisting biases, ultimately fragmenting any possibility of a collectively shared world-picture (Ferrara, 12 Nov 2024). In formal epistemology and collective intelligence theory, GETs are linked to failures of both internalist (reflective) and externalist (reliabilist) justification, producing what can be called “epistemic collapse” when the standards for what counts as warranted belief or actionable knowledge are globally degraded (Hila, 22 Dec 2025).

2. Taxonomies and Theoretical Frameworks

GETs have been characterized via multi-dimensional taxonomies in both social-epistemic and technical-regulatory frames.

Generative AI Risk Taxonomy (Ferrara): Four dimensions are identified (Ferrara, 12 Nov 2024):

1. Personal Loss: Identity theft, defamation, interpersonal or institutional trust erosion via synthetic likenesses.
2. Financial/Economic Damage: Fraud, market destabilization via synthetic or manipulated financial information.
3. Information Manipulation: Deepfakes, persuasive false narratives, and subliminal messaging embedded in media, aiming to shift group beliefs or behaviors.
4. Socio-technical/Infrastructural Risks: Systemic breakdowns (e.g. mass hyper-targeted realities leading to institutional collapse or algorithmic censorship).

Epistemic Injustice Subclasses (Mollema): Eleven interrelated forms, including participatory, contributory, testimonial (amplified/manipulative), and hermeneutical injustices, are mapped and extended with algorithmic/AI-specific variants such as generative hermeneutical erasure—a systematic loss of culturally situated concepts and interpretative frameworks under AI systems that present a “view from nowhere” (Mollema, 10 Apr 2025).

Algorithmic Epistemic Injustice (Kay et al.): Amplified/manipulative testimonial injustice, generative hermeneutical ignorance, and hermeneutical access injustice are formalized as generative-algorithmic dynamics that can globalize epistemic harm (Kay et al., 21 Aug 2024).

Collective Epistemology Framework: The distinction between internalist and externalist justification is central, with reflective knowledge (requiring accessible justificatory bases and epistemic duty) contrasted with the merely reliable transmission of truths (LLMs as “externalist reliabilists” with no internal justification) (Hila, 22 Dec 2025).

3. Formal Quantification and Metrics

GETs require explicit formalization to enable empirical assessment, risk modeling, and mitigation design.

• Epistemic Fragmentation Metrics (Ferrara): For individuals $i, j$, perceived fact distributions $P_i$, $P_j$, and ground truth $T$, fragmentation is captured by:

$$D_{\mathrm{JS}}(P_i \| P_j) = \tfrac12 D_{\mathrm{KL}}(P_i \| M) + \tfrac12 D_{\mathrm{KL}}(P_j \| M), \quad M = \tfrac12(P_i + P_j)$$

Average pairwise epistemic distance across a population, $$F = \frac{1}{N(N-1)} \sum_{i \neq j} D_{\mathrm{JS}}(P_i \| P_j)$$, measures global epistemic fragmentation. Truth-distortion rate is evaluated as $$E = \frac{1}{N}\sum_{i=1}^N D_{\mathrm{KL}}(P_i \| T)$$ (Ferrara, 12 Nov 2024).

• Epistemic Diversity and Knowledge Collapse: The effective number of meaning classes in LLM outputs is measured via the Hill-Shannon index,

$$S(X_{m,t}) = \exp\left\{ -\sum_{i\in X_{m,t}} p_i\ln p_i \right\}$$

where $p_i$ are observed claim-class probabilities. Lower $S$ indicates knowledge collapse (homogenization), an operational GET (Wright et al., 5 Oct 2025).

• Global UQ in Turbulence Modeling: Effectiveness ($\mu_j$) and inconsistency ($\sigma_j$) of RANS model perturbations across flow-parameter space diagnose GETs as candidate corrections with high effectiveness but high inconsistency—i.e., they fit at calibration points but generalize poorly, risking epistemic overfitting on a global scale (Huang et al., 2021).

4. Mechanisms and Empirical Manifestations

GETs emerge via multiple synergistic and mutually reinforcing mechanisms:

• Personalized Synthetic Realities: GenAI systems generate convincing but divergent worldviews and record-like artifacts (e.g., synthetic IDs or fake diplomatic events), each tailored to user profiles, resulting in epistemic atomization (Ferrara, 12 Nov 2024).
• Amplification and Manipulation: LLMs trained on biased or culturally restricted corpora amplify existing testimonial injustices and can be induced, via prompt engineering, to generate targeted disinformation or culturally erasing responses (Kay et al., 21 Aug 2024, Mollema, 10 Apr 2025).
• Structural and Algorithmic Erasure: LLMs trained on WEIRD-centric data present a “universal” perspective, leading to generative hermeneutical erasure—vanishing of community-specific concepts and epistemic resources (Mollema, 10 Apr 2025).
• Epistemic Norm Erosion through LLM Reliance: Wide adoption of LLM outputs as “reliable sources” undercuts reflective epistemic standards, diffuses ignorance, and propagates error, generating a feedback loop that further institutionally entrenches GETs (Hila, 22 Dec 2025).
• Disinformation Dynamics: Analytical modeling shows that, past critical thresholds, disinformation can induce a transition to a regime of pure epistemic isolation without viable social learning, an archetype GET in pandemic or collective action contexts (Tórtura et al., 2022).
• Knowledge Collapse and Cultural Bias: LLMs, especially larger models or those lacking robust retrieval augmentation, shrink epistemic diversity and overrepresent English or Western perspectives, leading to globalized knowledge collapse and regionally asymmetric representational justice failures (Wright et al., 5 Oct 2025).

5. Systemic Impacts and Cascading Effects

The magnitude and cascading potential of GETs arise from several factors:

• Marginal Cost and Commoditization: Zero or near-zero marginal cost for producing bespoke synthetic realities enables adversaries at all scales to mount large-scale epistemic attacks (Ferrara, 12 Nov 2024).
• Scale and Hyper-targeting: Mass micro-targeted misinformation or synthetic content can polarize populations, fragmenting the epistemic commons even within previously cohesive societies (Ferrara, 12 Nov 2024).
• Erosion of Institutional Trust: As deepfake or synthetic content becomes ubiquitous, the default public assumption shifts to skepticism toward all digital records, undermining fact-based discourse and any capacity for coordinated response to crises (Ferrara, 12 Nov 2024).
• Cultural and Linguistic Erasure: GETs can disproportionately affect minority or underrepresented groups, either silencing their epistemologies (hermeneutical death) or forcibly overwriting hinge concepts, reinforcing epistemic injustice globally (Mollema, 10 Apr 2025).
• Institutional and Collective Norm Shift: Declining standards for epistemic justification create a downward local-to-global feedback, whereby communities and institutions lower normative barriers, further exposing agents to GETs (Hila, 22 Dec 2025).

6. Detection, Measurement, and Mitigation Strategies

A growing literature advocates for multi-tiered and cross-sectoral responses to GETs, including technical, participatory, and policy interventions:

• Robust Provenance and Watermarking: Standardize cryptographically robust watermarking and lineage-tracking for GenAI outputs to allow detection and attribution (Ferrara, 12 Nov 2024, Kay et al., 21 Aug 2024).
• AI Forensic and Fact-checking Tools: Advance AI forensic methods capable of detecting manipulated or purely synthetic content, real-time deepfake detection, and scalable, evidence-based claims verification (Ferrara, 12 Nov 2024, Kay et al., 21 Aug 2024).
• Diversity Auditing and Culturally Specific Models: Employ epistemic-diversity audits, “knowledge islands,” and culturally fine-tuned LLMs to maintain pluralism and counter algorithmic homogenization (Wright et al., 5 Oct 2025, Mollema, 10 Apr 2025).
• Governance and Institutional Norms: Articulate governance frameworks—spanning model documentation, transparency mandates, shared “dos and don’ts,” and constitutional constraints at both organizational and legislative scales—to institutionalize epistemic resilience (Ferrara, 12 Nov 2024, Hila, 22 Dec 2025).
• Participatory Data Practices and Community Monitoring: Engage marginalized and underrepresented communities in co-design, data curation, and epistemic-vigilance forums, creating avenues for reporting, updating, and contesting erasure or misrepresentation (Mollema, 10 Apr 2025, Kay et al., 21 Aug 2024).
• Media Literacy and Public Education: Launch public campaigns to raise awareness of GETs, cultivate epistemic virtue, and foster independent verification practices (Ferrara, 12 Nov 2024, Hila, 22 Dec 2025).
• Retrieval-Augmented Generation: Integrate RAG pipelines with diverse, human-written corpora, and quarantine LLM-generated text to prevent epistemic feedback loops (Wright et al., 5 Oct 2025).

7. Open Research Challenges and Future Directions

Persisting gaps include:

• Operationalizing Fragmentation Metrics: Formal convergence of divergence-based metrics (e.g., Jensen–Shannon, Hill–Shannon) with real-world indicators of coalescence or polarization (Ferrara, 12 Nov 2024, Wright et al., 5 Oct 2025).
• Real-time Inference and Detection: Building deployed systems for real-time identification and disruption of personalized synthetic reality attacks, balancing privacy and detection efficacy (Ferrara, 12 Nov 2024).
• Reflective Justification and Institutional Epistemology: Restoring or enforcing reflective standards in collective and institutional knowledge work, especially in high-stakes or safety-critical contexts (Hila, 22 Dec 2025).
• Cross-cultural and Linguistic Resilience: Scaling epistemic representation metrics, developing multi-lingual and community-tuned models, and enshrining epistemic rights internationally (Mollema, 10 Apr 2025, Wright et al., 5 Oct 2025).
• Socio-technical and Infrastructural Design: Engineering epistemic “safe spaces,” provenance-verifiable communication channels, and contestable, open infrastructures to withstand systemic epistemic attack or collapse (Ferrara, 12 Nov 2024, Kay et al., 21 Aug 2024).
• Game-theoretic and Evolutionary Modeling: Further analysis of institutional strategies—regulatory, technical, and social—that can shift critical thresholds and stabilize trust in the face of adversarial disinformation (Tórtura et al., 2022).

In sum, GETs encapsulate existential risks posed by technologically and socially mediated epistemic failures. Addressing GETs requires empirical measurement, formal modeling, technical innovation, participatory system design, cross-sector collaboration, and international policy coordination. These efforts are fundamental to preserving collective epistemic infrastructure against the centrifugal forces of algorithmic fragmentation, disinformation, and cultural erasure.