Epistemic Profiles in Knowledge Systems

• Epistemic profiles are structured representations that articulate the scope, trust, and authority attributed to agents, artifacts, or knowledge claims.
• They are operationalized through methods such as codebook categorization in human–AI interaction, citation vector analysis in knowledge artifacts, and semantic embedding in bibliometrics.
• Formal models use epistemic logic and quantitative metrics to evaluate individual and collective knowledge breadth, influencing design and policy across knowledge ecosystems.

Epistemic profiles denote structured representations of epistemic status, scope, or authority in agents, artifacts, or knowledge products. Recent research operationalizes this concept for a range of analytic purposes: classifying the stance and status in human–AI relations (Yang et al., 2 Aug 2025), mapping the foundation of testimonial justification for collective knowledge claims (Mehdizadeh et al., 3 Dec 2025), measuring individual researchers’ thematic knowledge scope (Donner et al., 4 Nov 2024), and modeling skills required for knowledge possession in dynamic epistemic logic (Liang et al., 30 Oct 2024). Epistemic profiles thus serve as a crucial analytic tool in epistemology, HCI, bibliometrics, and epistemic logic.

1. Epistemic Profiles in Human–AI Interaction

In human–AI interaction, epistemic profiles (“epistemic relationships”) systematically capture how users attribute epistemic status, trust, and authority to AI across diverse tasks (Yang et al., 2 Aug 2025). Yang and Ma identify five empirically grounded epistemic relationships (ERs), each characterized along four principal axes: trust type, assessment mode, task type, and human epistemic status. These configurations are mapped by a five-part codebook.

ER Type	Metaphor	Assessment Mode
Instrumental Reliance	Tool	Outcome-based
Contingent Delegation	Assistant	Outcome-based
Co-agency Collaboration	Co-agent/Mentor	Mixed
Authority Displacement	Coach/Authority	Process-based
Epistemic Abstention	Tool (skeptical)	Outcome-based

• Instrumental Reliance: AI as an efficiency tool—users rely solely on outcome; no epistemic standing is ascribed.
• Contingent Delegation: AI as subordinate assistant—delegation is contingent, with human oversight and post-hoc validation.
• Co-agency Collaboration: AI treated as a genuine epistemic partner, supporting exploratory, interpretive work, evaluated via both process and outcome.
• Authority Displacement: AI is granted primary or partial epistemic authority, with process-centric assessment.
• Epistemic Abstention: Explicit epistemic reservation—AI is used only for convenience, with outputs habitually second-guessed or dismissed.

Epistemic profiles are dynamic; task properties, user experience, and the perceived reliability of AI systems modulate the user's position within this typology. The result is a multi-dimensional conceptual model for diagnosing and designing knowledge-centric human–AI collaborations (Yang et al., 2 Aug 2025).

2. Epistemic Profiles in Knowledge Artifacts

Mehdizadeh and Hilbert extend the epistemic profile concept to knowledge artifacts, specifically encyclopedia articles (Mehdizadeh et al., 3 Dec 2025). Here, an epistemic profile constitutes the structured composition of the article’s “web of testimony”—the institutional typology of its citations.

Eight exclusive citation categories define the article’s epistemic foundation:

• Academic & Scholarly (peer-reviewed)
• Government/Official
• News Journalism
• NGO / Civil Society
• Corporate / Commercial
• Opinion Advocacy
• Reference (Tertiary encyclopedic)
• User-Generated Content (UGC)

Each article is mapped to an 8-dimensional “epistemic vector” $[p_1,\dots,p_8]$, where $p_k$ is the proportion of citations in category $k$. Comparisons across articles and platforms (Wikipedia vs. Grokipedia) use Jensen–Shannon divergence and cosine similarity between these vectors.

Key analytical findings:

• Wikipedia prioritizes Academic (31.8%) and News (32.9%), while Grokipedia increases UGC, Government, NGO, and Corporate categories at the expense of Academic sources (dropping to 8.8%).
• Citation density in Grokipedia scales linearly with article length ($C = \beta_0 + \beta_1 L$ with $\beta_1 = 0.021$, $\mathrm{Adj}\,R^2 = 0.66$) and does not saturate, in contrast to Wikipedia.
• Topic sensitivity is acute: Grokipedia's epistemic profile closely mirrors Wikipedia for low-stakes leisure topics but diverges sharply for civic topics, producing distinct “bureaucratic triad” (Government, NGO, Corporate) citation cores and even disconnected “pop culture” vs “officialdom” knowledge regimes (Mehdizadeh et al., 3 Dec 2025).

3. Quantitative Measurement of Individual Epistemic Breadth

Researcher epistemic profiles—in the sense of thematic scope or “epistemic breadth”—are formalized via spatial bibliometrics (Donner et al., 4 Nov 2024). Donner and Blümel represent the profile as the spatial distribution of a researcher’s publications in a shared semantic vector space (using SPECTER 768-dimension embeddings). Pairwise cosine similarities between all papers yield measures of thematic proximity.

Principal metrics:

• Arithmetic mean similarity $B_{\mathrm{avg}}$
• Furthest-neighbor mean $B_{\mathrm{FN}}$
• Weighted furthest-neighbor mean $B_{\mathrm{WFN}}$
• Average shortest-path graph distance $B_{\mathrm{dist}}$

Validation:

• Known topic-switchers (HFSP fellows) have statistically smaller $B_{\mathrm{WFN}}$ than matched controls (Cohen’s $d \approx -0.85$), confirming increased epistemic breadth.
• Within a large German sample ($N>170{,}000$), narrower profiles correlate with higher realized self-reference and higher normalized component size in self-citation networks ($r=+0.40$).
• Embedding-based approaches avoid the arbitrariness of pre-defined topics and capture semantic relationships directly (Donner et al., 4 Nov 2024).

4. Formal Models: Epistemic-Logic–Based Profiles

Liang and Wáng introduce a logic of epistemic skills, where an agent's epistemic profile is given by a set of “skills” required to discriminate worlds and know formulas in a weighted epistemic model (Liang et al., 30 Oct 2024). The epistemic profile is expressible as the skill-set $C(a)$ and the agent’s knowledge relative to task-relevant skill requirements encoded by $E(w,u)$.

Key points:

• The epistemic-skills metric $\mathrm{SK}_i(\varphi)$ is the minimal number of additional skills agent $i$ requires at world $w$ to come to know $\varphi$.
• Upskilling ($(+S)_a$) and downskilling ($(-S)_a$) modify the agent’s epistemic profile.
• Model-checking problem remains tractable (in $\mathrm{P}$) in static fragments but becomes PSPACE-complete with quantification over skill-updates.
• Group epistemic profiles (knowledge distribution within collectives) are modeled via distributed, field, and common knowledge operators (Liang et al., 30 Oct 2024).

5. Epistemic Profile Classification and Measurement Frameworks

Unified formalization across contexts requires discrete or continuous representations:

• In human–AI studies, codebooks structure profiles along categorical axes (trust, assessment, role, expertise).
• In artifact analysis, distribution vectors over citation categories and network-theoretic properties (co-occurrence, homophily, entropy) measure epistemic substrate.
• In bibliometric science, vector geometry in semantic space operationalizes numeric breadth.
• In logic, skill-sets parametrize epistemic distinguishing power at a technical level.

Domain	Profile Representation	Primary Metric
Human–AI Interaction	Codebook categories	ER cluster membership (discrete typology)
Knowledge Artifacts	Citation category vector	JSD, entropy, homophily, scaling-law
Individual Research	Semantic vector geometry	Span, $B_{\mathrm{WFN}}, B_{\mathrm{dist}}$
Epistemic Logic	Skill set & update power	$\mathrm{SK}_i(\varphi)$, up/downskilling dynamics

6. Broader Theoretical and Practical Implications

The construction and analysis of epistemic profiles have direct consequences for authority, accountability, and the stability of knowledge ecosystems. In human–AI systems, the dynamic adaptation of epistemic roles complicates design for trust, responsibility, and authority transfer (Yang et al., 2 Aug 2025). In collective knowledge production, algorithmic epistemic infrastructures, such as generative AI encyclopedias, can reconfigure testimonial regimes and shift epistemic authority away from traditional peer-review and human deliberative practices—potentially fragmenting epistemic consistency across topics and platforms (Mehdizadeh et al., 3 Dec 2025). Quantitative and formal epistemic profile models also underpin evaluation and prediction of researcher interdisciplinarity, science policy, and the computational limits of group knowledge (Donner et al., 4 Nov 2024, Liang et al., 30 Oct 2024).

Ongoing research suggests systematic algorithmic audits and further refinement of embedding and logical models to safeguard epistemic stability, trace epistemic transitions, and inform design strategies for epistemically robust human–AI and collective knowledge systems.