Stability of AI Governance Systems: A Coupled Dynamics Model of Public Trust and Social Disruptions

Hou Liang; Jiaqi Lai; Weihong Huang

REVIEW 4 major objections 5 minor 18 references

The paper claims that the resilience of public trust in AI-driven governance has a precise mathematical threshold: a coupled trust-and-controversy dynamical system is stable exactly when the spectral radius of a fixed Jacobian matrix is les

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

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2026-08-02 18:28 UTC pith:LEZPWQFZ

load-bearing objection The algebra is careful and the FJ-Hawkes coupling is new, but the paper's own equations implement the opposite of its claimed collapse loop; the four abstract implications are not derived. the 4 major comments →

arxiv 2603.20248 v2 pith:LEZPWQFZ submitted 2026-03-10 cs.CY cs.AIcs.HCcs.MA

Stability of AI Governance Systems: A Coupled Dynamics Model of Public Trust and Social Disruptions

Jiaqi Lai , Hou Liang , Weihong Huang This is my paper

classification cs.CY cs.AIcs.HCcs.MA MSC 91D3039A30

keywords AI governanceinstitutional trusttrust collapsecoupled dynamicsspectral stabilityself-exciting controversiesnetwork topologystability analysis

verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper tries to establish that public trust in AI governance can be treated as a coupled dynamical system, where trust evolves through social influence and is buffeted by self-exciting controversy events, and that the boundary between resilience and collapse is exactly the spectral condition ρ(J2n)<1 on a 2n×2n Jacobian. The authors derive closed-form equilibria and show that event self-excitation and memory persistence shrink the stable region, so even minor algorithmic biases can, in principle, cascade into irreversible trust collapse without institutional intervention. A sympathetic reader would care because it converts the vague question of when the public loses faith in AI systems into a checkable eigenvalue condition on network topology and feedback parameters. The paper is explicitly a baseline collapse model, not an empirically calibrated one, and it draws four structural implications: high-trust systems can be fragile, low-trust systems can be stable, stability does not measure fairness or legitimacy, and network topology shapes equilibrium heterogeneity while its effect on spectral stability is bounded in a memory-dominated regime.

Core claim

The central claim is that governance stability reduces to a spectral criterion on coupled trust-event dynamics. By augmenting the state with a memory variable Ht that accumulates exponentially discounted past trust and event signals, the non-Markovian controversy process becomes a linear affine system with a constant Jacobian J2n = [[AW, B], [αI, (γ+β)I]]. The paper argues that local asymptotic stability holds if and only if ρ(J2n) < 1, and that crossing this boundary corresponds to a transition from trust resilience to systemic collapse. Closed-form fixed points for trust and controversy are derived, and in the decoupled case each network eigenmode contributes an independent pair of eigenva

What carries the argument

The central object is the augmented state vector x̂t = [Tt; Ht], where Tt is the vector of institutional trust across n agents and Ht is an auxiliary memory variable with recursive update H_{t+1} = γHt + αTt + βSt. This makes the Hawkes-like event process Markovian and yields a constant Jacobian whose eigenvalues set the stability boundary. The key identity is the nonlinear eigenvalue equation det(AW − α/(γ+β−λ)B − λI) = 0, which reduces to n independent quadratics when the network and sensitivity matrices are simultaneously diagonalizable. The spectral radius ρ(J2n) is the claimed resilience/collapse delimiter.

Load-bearing premise

The load-bearing premise is that the model's feedback signs match the collapse narrative—that declining trust amplifies controversy and controversy erodes trust—but the equations as written use positive α and, in the collapse experiment, positive B, which would make high trust generate more events and events raise trust; the four structural conclusions all depend on resolving this sign convention.

What would settle it

Re-run the Section 5.3.2 β-scan with B drawn from negative values (or with α < 0 instead of α > 0) and check whether divergence still appears at β* ≈ 0.50; if the instability disappears or the boundary shifts substantially, the claimed link between ρ(J2n)=1 and the collapse loop is an artifact of the sign choice.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

If this is right

Systems with ρ(J2n) < 1 return to their equilibrium trust profile after a finite perturbation, while systems with ρ(J2n) ≥ 1 turn small trust shocks into unbounded controversy escalation.
Increasing event self-excitation β or memory persistence γ narrows the stable parameter region; numerical experiments place the boundary near β* ≈ 0.50 and γ* ≈ 0.70 under the paper's chosen parameter configuration.
Stability is orthogonal to fairness and legitimacy: the model implies that a stable system can be unfair and an unstable system can be fair, so governance evaluation must track normative quality and structural recoverability separately.
Network topology shifts the stability boundary: echo-chamber structures lower the critical β compared with random or star networks, making trust more fragile under identical coupling parameters, while star networks are the most permissive.
The closed-form equilibrium solution lets long-run trust and controversy levels be computed directly from network structure and coupling parameters, providing a no-simulation baseline for diagnosing AI governance fragility.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Editorial inference: If the intended loop is 'low trust amplifies events and events erode trust,' the sign convention in the written equations must be reversed; the same spectral machinery would then describe distrust amplification rather than trust erosion, and the stability boundary would shift.
Editorial inference: The spectral criterion suggests an intervention target: rather than trying to raise trust directly, institutions could try to keep the effective self-excitation parameter below its critical value by damping media amplification and event cascades.
Editorial inference: A testable extension is to fit α, β, and γ to longitudinal trust surveys and controversy timelines; the model predicts specific stable versus divergent regimes from these fitted parameters, which could be checked against observed recovery or collapse episodes.
Editorial inference: The claimed bounded effect of topology in the memory-dominated regime invites a concrete numerical check: sweep γ close to 1 and compare critical β across random, echo-chamber, and star networks; if the critical values converge, the bound holds.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Desk Editor's Note

The algebra is careful and the FJ-Hawkes coupling is new, but the paper's own equations implement the opposite of its claimed collapse loop; the four abstract implications are not derived.

read the letter

Worth a look if you care about formal models of trust dynamics, but read it with the sign conventions in mind. The paper's central story — declining trust amplifies controversy, controversy erodes trust, forming a collapse loop — is not what its own equations do. In Eq. (2), S_{t+1} = ... + α T_i with α>0, so lower trust reduces future event intensity; and the β-collapse experiment forces B>0 so events raise trust. That's a self-correcting loop, not a collapse loop. This isn't a cosmetic miss: it flips the interpretation of the divergent regime and the meaning of ρ(J)<1.

What's genuinely good: the state augmentation that turns the non-Markovian memory into a 2n-dimensional linear system is clean, and the Schur complement reduction to the nonlinear eigenvalue problem is correct. The equilibrium closed forms and the decoupled quadratic eigenvalue expression are useful for later work. The algebra in the appendices checks out. The authors also honestly list the lack of empirical calibration and the linearity of the model as limitations.

Soft spots beyond the sign issue: the four 'structural implications' in the abstract (high-trust fragility, etc.) are asserted rather than derived in the body; §5.4 does give topology comparisons but only single runs, no error bars, and the stability boundary shift is acknowledged as slight. The β-scan's B>0 constraint is inconsistent with the general setup where B can have either sign. If the intended signs are α<0 and B<0, the spectral criterion is unchanged but the equilibrium values and the direction of divergence change, so the collapse interpretation needs a re-derivation.

Who this is for: researchers comfortable with linear systems theory who want a starting point for coupled FJ-Hawkes models. It deserves a serious referee — the formalism is repairable and the topic is important — but as written the central claim is contradicted by the model, so it needs major revision before publication.

Referee Report

4 major / 5 minor

Summary. The paper defines a discrete-time coupled system in which an n-vector T_t of institutional trust evolves by a Friedkin–Johnsen update plus an event term B S_t, and an n-vector S_t of controversy intensity follows a Hawkes-like accumulation S_{t+1} = μ + Σ_{i=0}^t γ^{t-i}(α T_i + β S_i). The authors introduce an auxiliary memory variable H_t, reduce the system to the affine form X_{t+1} = J X_t + const, derive a closed-form fixed point, and propose ρ(J_{2n}) < 1 as the stability/collapse threshold. They then run sensitivity and network-topology simulations. The algebraic core is largely correct: the state augmentation is a clean way to handle the non-Markovian memory, the Jacobian in Eq. (14) is right, and the Schur-complement reduction to the nonlinear eigenproblem in Eq. (15) is valid. However, the paper's central verbal claim—a self-reinforcing loop in which declining trust amplifies events and events erode trust—is not what Eqs. (1)–(2) implement. With α > 0 (the default and the scan range), lower trust decreases S_{t+1}; with B > 0 in the β-collapse experiment, events raise trust. The divergent regime in Fig. 5 is therefore a mutual amplification of trust and event intensity, not a trust collapse, and the four "structural implications" in the Abstract do not follow from the model as written.

Significance. If the intended feedback signs were specified and the instability regime were correctly interpreted, the framework could be a useful formal baseline for AI-governance trust dynamics: the spectral criterion is exact for the affine system and the memory augmentation is an elegant treatment of non-Markovian event histories. The paper also honestly lists limitations (linearity, homogeneity, no empirical calibration). However, as submitted, the main result is not supported by the paper's own equations. The sign convention is load-bearing and untreated: the model implements the opposite of the proposed governance-collapse mechanism. The contribution is currently a correct stability analysis of a system whose qualitative behavior is not the one described in the Abstract and Section 3.2.

major comments (4)

[§3.2, Eq. (2) and Abstract] The stated mechanism is that 'declining trust amplifies the intensity of subsequent controversy events ... forming a self-reinforcing collapse loop.' In the model, S_{t+1} = μ + Σ γ^{t-i}(α T_i + β S_i), so ∂S_{t+1}/∂T_t = α. With the paper's α ∈ [0,1] (default 0.005; scan 0 to 0.5 in §5.3.1), lower trust reduces future event intensity. The loop is reversed as written. If the intended sign is α < 0, it must be specified and used consistently in all derivations and simulations; as written, the Abstract's four implications and the 'collapse' interpretation are not consequences of the model.
[§5.3.2, Fig. 5] The β-collapse experiment constrains B ∼ U(0.01, 0.05) 'to ensure that social events have an amplifying effect on trust.' Thus the experiment intended to show the collapse transition actually uses events that raise trust. With α > 0, the divergence beyond β* is a runaway upward spiral in both T and S, not trust erosion. If B were negative as the verbal model requires, the off-diagonal block of J_{2n} changes sign; with α > 0 the cross-coupling product αB becomes negative and the spectrum, equilibrium (3)–(4), and critical β* differ. If both α and B are made negative, the spectral boundary is unchanged but the fixed-point values and the unstable direction change. The reported experiment does not exhibit collapse under any of these readings.
[§6.3] The paper equates ρ(J_{2n}) ≥ 1 with 'cascading mistrust' and 'irreversible collapse.' For this affine linear system, ρ > 1 means exponential divergence of the trajectory. Unless the model enforces the stated range T_t ∈ (0,2) through saturation or another nonlinear mechanism, instability is not collapse. The manuscript does not define collapse as a bounded-state phenomenon, so the real-world interpretation of the threshold is not established.
[Abstract and §5.4] The Abstract promises that 'network topology reshapes equilibrium heterogeneity while its effect on spectral stability is uniformly bounded in an explicit memory-dominated regime,' but no 'memory-dominated regime' is defined and no bound is stated or proved. Fig. 8 is a numerical comparison for three specific topologies. Either supply a precise formal statement and proof, or soften the Abstract claim.

minor comments (5)

[§6.2] The text refers to 'Equations (4.1.2.1)–(4.1.2.4)', but these equation numbers do not exist. Use the actual numbered equations from Section 4.1.
[Table 1] α is labeled 'Base event rate' but is used as the trust-to-event coupling; γ is labeled 'Trust sensitivity coefficient' but is used as the memory decay factor. These labels conflict with the equations and the surrounding text and should be corrected.
[§5.1 and §5.3] Section 5.1 writes S_0 = [0.1, ..., 0.1]^T, while Sections 5.3.1–5.3.3 write S_0 = 000...111. Use one consistent vector notation for initial conditions.
[Figures 2–6] 'showed' should be 'shown'; also, the figure captions do not specify the random generation procedure or seeds for A, W, and B, which limits reproducibility of the reported experiments.
[Reproducibility] No code or data are provided for the numerical experiments. Given the centrality of Figures 5–8 to the paper's conclusions, a reproducibility appendix with parameter tables or code would materially strengthen the manuscript.

Circularity Check

0 steps flagged

No significant circularity: the stability criterion and equilibria are derived algebraically from the stated coupled FJ–Hawkes equations; the paper contains no fitted parameters, no predictions from data, and no load-bearing self-citations.

full rationale

The derivation chain is self-contained. Equations (1) and (2) define the model; Section 4.1 solves the fixed-point equations algebraically (Appendix A), and Section 4.2/Appendices B–C construct the augmented-state Jacobian J2n and prove that local stability is equivalent to ρ(J2n)<1. This is a direct theorem of the linear/affine dynamics, not an input restated as an output. Numerical experiments in Section 5 vary parameters α, β, γ by hand and compare simulation outcomes to the analytic spectral radius; no parameter is fitted to data and then 'predicted.' The model's limitations (Section 6.5) explicitly state that the parameters 'remain theoretical' and have 'not yet been empirically calibrated,' which rules out fitted-input-called-prediction circularity. References to Friedkin–Johnsen and Hawkes models are external foundational citations, not self-citations, and no uniqueness claim is imported from the authors' own prior work. The sign-convention concern (α>0 and B>0 in Eqs. (1)–(2) produce event-driven trust amplification rather than erosion) is an assumption/soundness issue about whether the equations match the verbal mechanism; it does not make the derivation circular, because the spectral criterion follows from the equations as written. Accordingly no circular step is exhibited.

Axiom & Free-Parameter Ledger

8 free parameters · 5 axioms · 1 invented entities

All scalar parameters and matrix distributions are chosen by hand; the paper explicitly states it has not been empirically calibrated. The sign of B and α is the most consequential ad hoc choice because it reverses the claimed feedback loop.

free parameters (8)

α (trust-to-event coupling) = baseline 0.005; scans 0-0.5
Chosen by hand, not estimated; controls how trust feeds event memory (Eq. 2). No empirical basis (Section 6.5).
β (event self-excitation) = baseline 0.4; critical ≈0.50 in Fig. 5
Chosen by hand; determines stability boundary. No data fit.
γ (memory decay) = baseline 0.5; critical ≈0.70 in Fig. 6
Chosen by hand; controls memory persistence.
μ (baseline event rate) = 0.1
Chosen by hand; constant background controversy intensity.
A (stubbornness matrix) = diagonal entries ~ U(0.4,0.9)
Randomly drawn rather than measured; FJ parameter.
W (influence matrix) = row-normalized random, echo-chamber, star matrices
Hand-specified topologies, one realization each; not calibrated to empirical networks.
B (event reactivity matrix) = baseline b_i~U(-0.05,0.05); β-scan b_i~U(0.01,0.05)
Sign choice flips the feedback direction; no empirical justification.
Initial/reference trust T(0), T1 = sampled U(0,2)
Initial conditions set by hand; equilibrium depends on T1 through X.

axioms (5)

standard math Spectral radius criterion for linear discrete-time dynamical systems
Used in Section 4.2; standard result.
domain assumption Friedkin-Johnsen update with fixed susceptibility and influence matrices is a valid model of institutional trust
Eq. (1); no empirical test.
domain assumption Controversy intensity is a linear exponentially-memory-weighted sum of past trust and events
Eq. (2); linearity and exponential memory are asserted, not derived.
ad hoc to paper Sign convention α>0, and B>0 in the collapse scan, represents the intended feedback
This is the load-bearing sign choice; it reverses the stated collapse loop.
domain assumption Perceived event intensity S_t enters trust additively through B
Eq. (1); no saturation or nonlinearity.

invented entities (1)

Auxiliary memory vector H_t no independent evidence
purpose: Turns the non-Markovian sum in Eq. (2) into a Markovian state so a constant Jacobian can be written
A mathematical device only; no falsifiable consequence outside the model.

reviewed 2026-08-02 · how reviews work

0 comments

read the original abstract

AI systems are increasingly entrenched in public governance, yet scholarship lacks formal tools to determine when deviations of public trust in algorithmic institutions dissipate and when they grow into collapse. Stability refers here to asymptotic recovery from finite state perturbations under fixed structural parameters. We address this gap by developing a mathematical framework for institutional trust stability that couples a Friedkin-Johnsen opinion dynamics process with a Hawkes-inspired intensity process for AI controversies. Motivated by the Computers-Are-Social-Actors literature and recent studies of trust in large language models, this bidirectional coupling reveals that governance stability depends on the structural architecture of the information environment rather than absolute trust levels. We derive an exact spectral stability criterion delineating resilience from collapse, demonstrating how event self-excitation and memory persistence systematically narrow the stable parameter regime. Our structural analysis yields four counterintuitive structural implications: high-trust systems can be structurally fragile, low-trust environments can be structurally stable, dynamical stability neither measures nor guarantees algorithmic fairness or legitimacy, and network topology reshapes equilibrium heterogeneity while its effect on spectral stability is uniformly bounded in an explicit memory-dominated regime. Governance assessment should therefore pair normative evaluation of harms and fairness with structural analysis of recoverability, rather than treating either as a proxy for the other.

Figures

Figures reproduced from arXiv: 2603.20248 by Hou Liang, Jiaqi Lai, Weihong Huang.

**Figure 1.** Figure 1: Coupled dynamics between trust and perceived event intensity nitive bias, or risk attitude. The product BSt thus models a realistic two-stage process: agents first perceive events differently, then react to those perceptions differently, jointly shaping the trajectory of trust across the population. 4. Theoretical Analysis 4.1 Equilibrium Analysis To analyze the steady-state behavior of the trust-event dy… view at source ↗

**Figure 2.** Figure 2: Simulation with time step=50 [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗

**Figure 3.** Figure 3: Simulation with time step=5 discussed in Section 6—a modest increase in event self-excitation can push the system from stable convergence to unbounded amplification. 5.3.3 Impact of γ on System Memory. The parameter γ modulates the decay rate of the past influence in the social event dynamics. It effectively determines the system’s memory horizon. To understand its role, we conduct a sensitivity analysi… view at source ↗

**Figure 4.** Figure 4: Sensitivity analysis of α (β = 0.4, γ = 0.5). The system remains stable (ρ(J2n) < 1) throughout the scanned range α ∈ [0,0.5], as shown in the right panel [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗

**Figure 5.** Figure 5: Sensitivity analysis of β (γ = 0.5, α = 0.05). The dashed red line marks the stability boundary ρ(J2n) = 1 at β ∗ ≈ 0.50; the shaded region indicates parameter values for which the system diverges. The right panel shows the spectral radius ρ(J2n) as a function of β. jectories Si(t), and influence matrix heatmaps for all three topologies. Several observations emerge: • In the random network, trust trajecto… view at source ↗

**Figure 6.** Figure 6: Sensitivity analysis of γ (β = 0.3, α = 0.05). The dashed red line marks the stability boundary ρ(J2n) = 1 at γ ∗ ≈ 0.70; the shaded region indicates divergent parameter values. The right panel shows ρ(J2n) as a function of γ. coefficients α, β - interact to determine steady-state trust and event intensity. In real-world applications, the equilibrium solution can be interpreted as the outcome of a sustain… view at source ↗

**Figure 7.** Figure 7: Effect of network topology on trust-event dynamics (n = 10, α = 0.05, β = 0.35, γ = 0.5). Columns correspond to three network structures: Random, Echo Chamber (2 clusters), and Star/KOL. Top row: trust trajectories Ti(t); middle row: perceived event intensity Si(t); bottom row: influence matrix W heatmap. The spectral radius ρ(J2n) is reported for each topology. cation triggers a Hawkes-type cascade: each … view at source ↗

**Figure 8.** Figure 8: Stability boundaries across network topologies. Each panel shows ρ(J2n) as a function of one parameter (β, γ, or α) with the others fixed. The dashed red line marks the stability threshold ρ(J2n) = 1. Different topologies exhibit different critical parameter values, demonstrating the role of network structure in determining system resilience. media cycles or policy interventions. Without accounting for ran… view at source ↗

discussion (0)

Reference graph

Works this paper leans on

18 extracted references · 2 canonical work pages

[3]

Mehrdad Farajtabar, Yichen Wang, Manuel Gomez-Rodriguez, Sushant Li, Hongyuan Zha, and Le Song

Edelman Global. Mehrdad Farajtabar, Yichen Wang, Manuel Gomez-Rodriguez, Sushant Li, Hongyuan Zha, and Le Song. Coevolve: A joint point process model for information diffusion and network co-evolution. InAdvances in Neural Information Processing Systems, pages 1954–1962,

1954
[13]

Denise M

URL https://arxiv.org/abs/1708.06401. Denise M. Rousseau, Sim B. Sitkin, Ronald S. Burt, and Colin Camerer. Not so different after all: A cross-discipline view of trust.Academy of Management Review, 23(3):393–404,

Pith/arXiv arXiv
[15]

Paul Slovic

doi: 10.1145/3287560.3287598. Paul Slovic. Perceived risk, trust, and democracy.Risk Analysis, 13(6): 675–682,

arXiv
[17]

Qingyuan Zhao, Murat A

doi: 10.1073/pnas.082090499. Qingyuan Zhao, Murat A. Erdogdu, Hsi-Yuan He, Anand Rajaraman, and Jure Leskovec. Seismic: A self-exciting point process model for predict- ing tweet popularity. InProceedings of the 21th ACM SIGKDD Inter- national Conference on Knowledge Discovery and Data Mining, pages 1513–1522,

doi:10.1073/pnas.082090499
[1971]

doi: 10.1093/biomet/58.1.83. Kevin A. Hoff and Masooda Bashir. Trust in automation: Integrating em- pirical evidence on factors that influence trust.Human Factors, 57(3): 407–434,

doi:10.1093/biomet/58.1.83
[1993]

Michael Taylor

doi: 10.1111/j.1539-6924.1993.tb01329.x. Michael Taylor. Towards a mathematical theory of influence and attitude change.Human Relations, 21(2):121–139,

arXiv 1993
[1995]

doi: 10.5465/amr.1995.9508080335. D. Harrison McKnight, Vivek Choudhury, and Charles Kacmar. The impact of initial consumer trust on intentions to transact with a web site: A trust building model.The Journal of Strategic Information Systems, 11(3–4): 297–323,

arXiv 1995
[1998]

Andrew D

doi: 10.2307/259284. Andrew D. Selbst, Danah Boyd, Sorelle A. Friedler, Suresh Venkatasub- ramanian, and Janet Vertesi. Fairness and abstraction in sociotechnical systems. InProceedings of the Conference on Fairness, Accountability, and Transparency, pages 59–68,

work page doi:10.2307/259284
[2002]

Sergey E

doi: 10.1016/S0963-8687(02)00020-3. Sergey E. Parsegov, Alexey V . Proskurnikov, Roberto Tempo, and Noah E. Friedkin. Novel multidimensional models of opinion dynamics in social networks.IEEE Transactions on Automatic Control, 62(5):2270–2285,

doi:10.1016/s0963-8687(02)00020-3
[2004]

46.1.50_30392

doi: 10.1518/hfes. 46.1.50_30392. J. Ma and T. Wu. Social network group decision-making model considering interactions between trust relationships and opinion evolution.Kyber- netes,

doi:10.1518/hfes
[2005]

Izak Benbasat and Wendy Wang

doi: 10.1038/nature03459. Izak Benbasat and Wendy Wang. Trust in and adoption of online recom- mendation agents.Journal of the Association for Information Systems, 6 (3):72–101,

doi:10.1038/nature03459
[2009]

doi: 10.1016/j.jbusres.2008.06.016. Alan G. Hawkes. Spectra of some self-exciting and mutually exciting point processes.Biometrika, 58(1):83–90,

work page doi:10.1016/j.jbusres.2008.06.016 2008
[2015]

doi: 10.1145/2783258.2783401. 15

arXiv
[2016]

White, Margaret Mitchell, Timnit Gebru, Ben Hutchinson, Jamila Smith-Loud, Daniel Theron, and Parker Barnes

Inioluwa Deborah Raji, Andrew Smart, Rebecca N. White, Margaret Mitchell, Timnit Gebru, Ben Hutchinson, Jamila Smith-Loud, Daniel Theron, and Parker Barnes. Closing the AI accountability gap: Defin- ing an end-to-end framework for internal algorithmic auditing. InPro- ceedings of the 2020 Conference on Fairness, Accountability, and Trans- parency, pages 33–44,

2020
[2018]

doi: 10.1007/s11023-018-9482-5. Noah E. Friedkin and Eugene C. Johnsen. Social influence and opinions. Journal of Mathematical Sociology, 15(3-4):193–206,

doi:10.1007/s11023-018-9482-5
[2019]

doi: 10.1038/s42256-019-0088-2. John D. Lee and Katrina A. See. Trust in automation: Designing for appro- priate reliance.Human Factors, 46(1):50–80,

doi:10.1038/s42256-019-0088-2
[2020]

Marian-Andrei Rizoiu, Young Lee, Swapnil Mishra, and Lexing Xie

doi: 10.1145/3351095.3372873. Marian-Andrei Rizoiu, Young Lee, Swapnil Mishra, and Lexing Xie. A tutorial on hawkes processes for events in social media.arXiv preprint, arXiv:1708.06401,

arXiv
[2023]

https://www.edelman.com/trust/ 2023-trust-barometer,

2023

This paper was first reviewed by deepseek-v4-flash on August 2, 2026.

[1] [3]

Mehrdad Farajtabar, Yichen Wang, Manuel Gomez-Rodriguez, Sushant Li, Hongyuan Zha, and Le Song

Edelman Global. Mehrdad Farajtabar, Yichen Wang, Manuel Gomez-Rodriguez, Sushant Li, Hongyuan Zha, and Le Song. Coevolve: A joint point process model for information diffusion and network co-evolution. InAdvances in Neural Information Processing Systems, pages 1954–1962,

1954

[2] [13]

Denise M

URL https://arxiv.org/abs/1708.06401. Denise M. Rousseau, Sim B. Sitkin, Ronald S. Burt, and Colin Camerer. Not so different after all: A cross-discipline view of trust.Academy of Management Review, 23(3):393–404,

Pith/arXiv arXiv

[3] [15]

Paul Slovic

doi: 10.1145/3287560.3287598. Paul Slovic. Perceived risk, trust, and democracy.Risk Analysis, 13(6): 675–682,

arXiv

[4] [17]

Qingyuan Zhao, Murat A

doi: 10.1073/pnas.082090499. Qingyuan Zhao, Murat A. Erdogdu, Hsi-Yuan He, Anand Rajaraman, and Jure Leskovec. Seismic: A self-exciting point process model for predict- ing tweet popularity. InProceedings of the 21th ACM SIGKDD Inter- national Conference on Knowledge Discovery and Data Mining, pages 1513–1522,

doi:10.1073/pnas.082090499

[5] [1971]

doi: 10.1093/biomet/58.1.83. Kevin A. Hoff and Masooda Bashir. Trust in automation: Integrating em- pirical evidence on factors that influence trust.Human Factors, 57(3): 407–434,

doi:10.1093/biomet/58.1.83

[6] [1993]

Michael Taylor

doi: 10.1111/j.1539-6924.1993.tb01329.x. Michael Taylor. Towards a mathematical theory of influence and attitude change.Human Relations, 21(2):121–139,

arXiv 1993

[7] [1995]

doi: 10.5465/amr.1995.9508080335. D. Harrison McKnight, Vivek Choudhury, and Charles Kacmar. The impact of initial consumer trust on intentions to transact with a web site: A trust building model.The Journal of Strategic Information Systems, 11(3–4): 297–323,

arXiv 1995

[8] [1998]

Andrew D

doi: 10.2307/259284. Andrew D. Selbst, Danah Boyd, Sorelle A. Friedler, Suresh Venkatasub- ramanian, and Janet Vertesi. Fairness and abstraction in sociotechnical systems. InProceedings of the Conference on Fairness, Accountability, and Transparency, pages 59–68,

work page doi:10.2307/259284

[9] [2002]

Sergey E

doi: 10.1016/S0963-8687(02)00020-3. Sergey E. Parsegov, Alexey V . Proskurnikov, Roberto Tempo, and Noah E. Friedkin. Novel multidimensional models of opinion dynamics in social networks.IEEE Transactions on Automatic Control, 62(5):2270–2285,

doi:10.1016/s0963-8687(02)00020-3

[10] [2004]

46.1.50_30392

doi: 10.1518/hfes. 46.1.50_30392. J. Ma and T. Wu. Social network group decision-making model considering interactions between trust relationships and opinion evolution.Kyber- netes,

doi:10.1518/hfes

[11] [2005]

Izak Benbasat and Wendy Wang

doi: 10.1038/nature03459. Izak Benbasat and Wendy Wang. Trust in and adoption of online recom- mendation agents.Journal of the Association for Information Systems, 6 (3):72–101,

doi:10.1038/nature03459

[12] [2009]

doi: 10.1016/j.jbusres.2008.06.016. Alan G. Hawkes. Spectra of some self-exciting and mutually exciting point processes.Biometrika, 58(1):83–90,

work page doi:10.1016/j.jbusres.2008.06.016 2008

[13] [2015]

doi: 10.1145/2783258.2783401. 15

arXiv

[14] [2016]

White, Margaret Mitchell, Timnit Gebru, Ben Hutchinson, Jamila Smith-Loud, Daniel Theron, and Parker Barnes

Inioluwa Deborah Raji, Andrew Smart, Rebecca N. White, Margaret Mitchell, Timnit Gebru, Ben Hutchinson, Jamila Smith-Loud, Daniel Theron, and Parker Barnes. Closing the AI accountability gap: Defin- ing an end-to-end framework for internal algorithmic auditing. InPro- ceedings of the 2020 Conference on Fairness, Accountability, and Trans- parency, pages 33–44,

2020

[15] [2018]

doi: 10.1007/s11023-018-9482-5. Noah E. Friedkin and Eugene C. Johnsen. Social influence and opinions. Journal of Mathematical Sociology, 15(3-4):193–206,

doi:10.1007/s11023-018-9482-5

[16] [2019]

doi: 10.1038/s42256-019-0088-2. John D. Lee and Katrina A. See. Trust in automation: Designing for appro- priate reliance.Human Factors, 46(1):50–80,

doi:10.1038/s42256-019-0088-2

[17] [2020]

Marian-Andrei Rizoiu, Young Lee, Swapnil Mishra, and Lexing Xie

doi: 10.1145/3351095.3372873. Marian-Andrei Rizoiu, Young Lee, Swapnil Mishra, and Lexing Xie. A tutorial on hawkes processes for events in social media.arXiv preprint, arXiv:1708.06401,

arXiv

[18] [2023]

https://www.edelman.com/trust/ 2023-trust-barometer,

2023