Trust Erosion Dynamics Model

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LLM Summary:Models how AI systems accelerate trust erosion through deepfakes, disinformation, and authentication collapse, finding trust erodes 3-10x faster than it builds. With US institutional trust at 18-30% approaching critical thresholds (below 20% = governance failure) and 245% YoY growth in deepfake misinformation, the model projects 35% probability of accelerated erosion to 15-25% institutional trust within 5-10 years.

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Model

Trust Erosion Dynamics Model

Importance52

Model TypeTrust Dynamics

Target FactorTrust Erosion

Key InsightTrust erodes faster than it builds, with 3-10x asymmetry in speed

Parameters

Risks

Trust Decline

Models

Trust Cascade Failure Model

Model Quality

Novelty

4.5

Rigor

Actionability

Completeness

6.5

Overview

This model examines how AI systems contribute to the erosion of trust in institutions, experts, and interpersonal relationships. The 2025 KPMG/University of Melbourne global study found that while 66% of people use AI regularly, only 46% globally are willing to trust AI systems—and trust levels have declined as adoption has increased. Meanwhile, the 2025 Edelman Trust Barometer documented that multi-decade institutional trust erosion has accelerated into what researchers call a “grievance” phenomenon, with 61% of respondents globally reporting moderate to high grievance levels.

The central question this model addresses: How do AI systems erode trust, and why is trust so difficult to rebuild once lost? The answer lies in a fundamental asymmetry: trust erodes 3-10x faster than it builds, and AI technologies dramatically accelerate erosion mechanisms while offering few pathways to restoration.

Trust Erosion Framework

The following diagram illustrates how AI-driven erosion mechanisms cascade through different trust domains, ultimately producing self-reinforcing distrust cycles.

Loading diagram...

Types of Trust Affected

1. Institutional Trust

Trust in government, media, science, corporations
Eroded by: AI-enabled manipulation, deepfakes, surveillance
Consequences: Governance breakdown, policy resistance

2. Expert Trust

Trust in professionals, specialists, authorities
Eroded by: AI competing with experts, AI errors attributed to experts
Consequences: Ignoring expert advice, dangerous self-reliance

3. Information Trust

Trust in media, facts, shared reality
Eroded by: Deepfakes, AI-generated misinformation, authentication failures
Consequences: Epistemic fragmentation, inability to coordinate

4. Interpersonal Trust

Trust in other individuals, social relationships
Eroded by: AI impersonation, synthetic relationships, surveillance
Consequences: Social atomization, reduced cooperation

5. Technology Trust

Trust in AI and technology systems themselves
Eroded by: AI failures, unexpected behaviors, opacity
Consequences: Resistance to beneficial AI, or paradoxically, excessive trust

Erosion Mechanisms

1. Deepfake Reality Distortion

Mechanism: AI-generated synthetic media makes it impossible to trust visual/audio evidence. Globally, deepfake-related misinformation rose by 245% year-over-year in 2024, with spikes in countries holding major elections.

Process:

Deepfakes become increasingly convincing—research confirms humans cannot consistently identify AI-generated voices
Authentic content becomes indistinguishable from fake
All evidence becomes suspect (the “liar’s dividend” allows dismissal of authentic recordings as probable fakes)
Visual evidence loses probative value in legal and journalistic contexts

Trust Impact:

Media trust: Severe erosion—Deloitte’s 2024 study found 50% of respondents more skeptical of online information than a year ago
Legal evidence trust: Significant erosion
Interpersonal trust: Growing concern—68% of those familiar with generative AI report concern about deceptive synthetic content

Current Status: Early-to-mid stage; detection still possible but rapidly declining. While 57% of people believe they could spot a deepfake, research suggests this confidence is misplaced for high-quality synthetic media.

Timeline:

2020-2023: Detectable deepfakes, limited impact
2024-2026: Near-undetectable deepfakes, significant impact; fraud losses projected to grow from $12.3B (2023) to $40B (2027)
2027+: Post-authenticity era (assuming no breakthrough in verification)

2. AI-Enabled Disinformation

Mechanism: AI dramatically scales and personalizes disinformation campaigns.

Process:

AI generates vast quantities of convincing misinformation
Personalization makes disinformation more persuasive
Detection cannot keep pace with generation
Information environment becomes unreliable

Trust Impact:

Media trust: Severe erosion
Platform trust: Moderate erosion
Peer information trust: Moderate erosion

Current Status: Active and accelerating

3. Surveillance Chilling Effects

Mechanism: Awareness of AI surveillance erodes trust in private communication and institutions.

Process:

AI enables pervasive surveillance (facial recognition, communications monitoring)
People assume they are being watched
Self-censorship and guardedness increase
Authentic interaction and trust formation impaired

Trust Impact:

Government trust: Severe erosion (in surveillance states)
Institutional trust: Moderate erosion
Interpersonal trust: Moderate erosion

Current Status: Severe in authoritarian contexts, emerging in democracies

4. Expert Displacement

Mechanism: AI competing with and sometimes outperforming human experts undermines expert trust.

Process:

AI provides faster, sometimes better answers than experts
But AI also makes confident errors
Unclear when to trust AI vs. human expert
Both AI and human expert trust become uncertain

Trust Impact:

Expert trust: Moderate erosion
Professional institution trust: Moderate erosion

Current Status: Emerging, accelerating with LLM adoption

5. Authentication Collapse

Mechanism: AI impersonation undermines ability to verify identity and authenticity.

Process:

AI can impersonate voices, faces, writing styles
Traditional authentication methods fail
Impossible to verify identity of remote communications
Fundamental interpersonal trust undermined

Trust Impact:

Interpersonal trust: Potentially severe erosion
Transaction trust: Moderate erosion
Legal identity trust: Growing concern

Current Status: Early stage but accelerating

Model Parameters

The following table quantifies key parameters in the trust erosion model, drawing on recent survey data and research.

Parameter	Best Estimate	Range	Confidence	Source
Global AI trust rate	46%	32-72%	High	KPMG/Melbourne 2025
US AI trust rate	32%	25-40%	High	Edelman 2025
Trust erosion/building asymmetry	5x	3-10x	Medium	Trust asymmetry research
Deepfake misinformation growth (YoY)	245%	150-350%	Medium	Deloitte 2024
Public concern about AI (US)	50%	45-55%	High	Pew Research 2025
High grievance population (global)	61%	55-67%	High	Edelman 2025
AI contribution to trust decline	15-25%	5-40%	Low	Model estimate
Detection accuracy for deepfakes	Below 60%	40-65%	Medium	iProov 2024

Trust Dynamics Model

Building vs. Eroding Trust

Trust Building:

Slow, cumulative process
Requires repeated positive interactions
Depends on vulnerability and follow-through
Takes years to build strong trust

Trust Erosion:

Can be rapid (single betrayal)
Negative events weighted more than positive
Cascades through networks (distrust spreads)
Generalized from specific failures

Asymmetry: Trust erodes faster than it builds. Research on trust asymmetry confirms the adage that “trust goes on horseback and goes back on foot”—negative information has a greater impact on trust levels than equivalent positive information. Estimated 3-10x asymmetry in speed, with the ratio higher for institutional trust than interpersonal trust.

The Trust Cascade

Single Trust Failure
  ↓ (Generalization)
Category Trust Erosion (e.g., distrust one news source → distrust all news)
  ↓ (Expansion)
Institutional Trust Erosion (distrust media → distrust government)
  ↓ (Network Effects)
Social Trust Erosion (nobody can be trusted)
  ↓ (Feedback)
Self-reinforcing distrust (distrust causes behaviors that confirm distrust)

Quantitative Framework

Trust level $T$ decays over time with negative events according to the following dynamics:

$T(t) = T_0 \cdot e^{-\lambda n(t)} \cdot (1 - \gamma C)$

Where:

$T_0$ = Initial trust level (0 to 1)
$\lambda$ = Erosion rate per negative event (0.1-0.5 for institutional trust)
$n(t)$ = Cumulative negative events experienced
$\gamma$ = Cascade multiplier (0.2-0.8)
$C$ = Cross-domain contamination factor (0 to 1)

The key insight is that erosion is multiplicative while rebuilding is additive. A single high-profile trust violation (deepfake-enabled fraud, institutional deception revealed) can reduce trust by 20-40%, while rebuilding requires hundreds of positive interactions over years.

Trust Threshold Analysis

Critical Trust Thresholds

Trust Type	Warning Level	Critical Level	Consequences at Critical
Institutional	Below 40%	Below 20%	Governance failure
Expert	Below 50%	Below 30%	Public health/safety crises
Information	Below 40%	Below 25%	Epistemic fragmentation
Interpersonal	Below 60%	Below 40%	Social breakdown
Technology	Below 30% or above 80%	Extremes	Either rejection or dangerous over-reliance

Current Trust Levels (US Estimates)

Trust Type	2010 Level	2020 Level	2025 Level	Trend
Government trust	22%	20%	18%	Declining
Media trust	32%	29%	24%	Declining
Science trust	70%	65%	58%	Declining
Tech company trust	45%	35%	30%	Declining
AI trust (US)	N/A	N/A	32%	Baseline
Interpersonal trust	48%	42%	38%	Declining

Note: Estimates based on Edelman 2025, Pew Research 2025, and Gallup surveys. Five of the 10 largest global economies (Japan, Germany, UK, US, France) are among the least trusting nations. In contrast, China reports 72% AI trust.

Approach to Critical Thresholds

Trust Type	Distance to Critical	Estimated Time (current trend)
Government	Near critical	Already at risk
Media	Near critical	3-7 years
Science	Moderate buffer	10-20 years
Tech companies	Moderate	5-10 years
Interpersonal	Some buffer	10-15 years

Scenario Analysis

The trajectory of trust erosion depends on technological developments, regulatory responses, and institutional adaptation. The following scenarios represent plausible futures over the next decade.

Scenario	Probability	Trust Outcome	Key Drivers	Implications
Managed Authentication	20%	Stabilization at 35-45% institutional trust	Successful content provenance standards, cryptographic verification widely adopted	Epistemic commons preserved but fragmented; trust rebuilding possible over 20-30 years
Accelerated Erosion	35%	Decline to 15-25% institutional trust	Deepfake detection fails, no regulatory coordination, synthetic content indistinguishable	Post-truth environment; governance based on identity rather than evidence; high polarization
Two-Tier Trust	30%	Elite trust at 50-60%, mass trust at 20-30%	Verification tools available but expensive/complex; digital literacy gap widens	Functional elite discourse; mass populations susceptible to manipulation; increased inequality
Trust Collapse Crisis	15%	Rapid decline below 15% after major incident	High-profile deepfake causes international crisis or mass casualty event; authentication systems compromised	Emergency governance measures; potential for authoritarian responses; social cohesion breakdown

The most likely trajectory (55% combined probability) involves continued trust erosion with varying degrees of mitigation, reaching critical thresholds for government and media trust within 5-10 years. The Urban Institute’s 2024 analysis notes that trust erosion is “intertwined with broader issues of polarization, gridlock, and social malaise,” suggesting that AI-specific interventions alone cannot reverse the trend.

Trust Rebuilding Challenges

Why Trust is Hard to Rebuild

1. Betrayal Trauma

Trust violations are remembered longer than trust-building
Emotional weight of betrayal persists
Risk aversion increases after violation

2. Changed Baseline

Once trust is lost, default becomes distrust
Burden of proof shifts to trustee
Every interaction scrutinized

3. Confirmation Bias

Distrust looks for evidence of untrustworthiness
Positive evidence discounted
Negative evidence amplified

4. Collective Action Problem

Individual trustworthiness insufficient
Need systemic change to rebuild institutional trust
Coordination difficult when trust is low

5. Generational Effects

Those who experienced trust violation never fully trust
Younger generations may have higher baseline distrust
Cultural transmission of distrust

Rebuilding Requirements

Factor	Importance	Difficulty
Acknowledged wrongdoing	Essential	Medium
Structural change	Very High	Very High
Consistent behavior over time	Essential	High
Transparency	High	Medium
Accountability	High	High
Time	Essential	Inherent

Estimated Rebuilding Time:

Minor trust violation: Months to years
Moderate violation: Years to decades
Severe systemic violation: Generations
Some violations: May be permanent within living memory

Intervention Strategies

Preventing Erosion

1. Authenticity Infrastructure

Develop robust content provenance systems
Create identity verification mechanisms
Invest in deepfake detection and watermarking
Challenge: Technical arms race, adoption barriers

2. Transparency and Accountability

Require disclosure of AI use and capabilities
Implement algorithmic accountability
Create meaningful oversight mechanisms
Challenge: Conflicts with business interests

3. Media Literacy and Epistemic Resilience

Education on information evaluation
Critical thinking training
Healthy skepticism without cynicism
Challenge: Scale, reaching vulnerable populations

Slowing Erosion

4. Platform Responsibility

Hold platforms accountable for amplifying distrust
Require moderation of trust-eroding content
Incentivize trust-building features
Challenge: Free speech concerns, business models

5. Institutional Reform

Address legitimate grievances driving distrust
Increase transparency and responsiveness
Demonstrate trustworthiness through action
Challenge: Institutional resistance to change

Rebuilding Trust

6. Long-term Commitment

Accept that rebuilding takes years/decades
Consistent trustworthy behavior over time
No shortcuts to restored trust
Challenge: Political/business cycles shorter than needed

7. New Trust Mechanisms

Decentralized verification systems
Reputation mechanisms
Community-based trust networks
Challenge: May not scale, vulnerable to gaming

Model Limitations

1. Cultural Variation

Trust dynamics vary across cultures
Baseline trust levels differ
Model calibrated primarily on Western/US context

2. Measurement Challenges

Trust difficult to measure precisely
Survey responses may not reflect behavior
Different definitions across studies

3. Causation Complexity

AI is one factor among many eroding trust
Isolating AI-specific effects difficult
Political, economic factors also significant

4. Prediction Uncertainty

Trust behavior in novel situations hard to predict
Tipping points may exist but are hard to identify
Future AI capabilities uncertain

5. Rebuilding Understudied

Less research on rebuilding than erosion
Historical analogies may not apply
AI-specific rebuilding strategies unknown

Uncertainty Ranges

Parameter	Best Estimate	Range	Confidence
Erosion/building rate asymmetry	5x	3-10x	Medium
Current US institutional trust	20-30%	15-40%	Medium
Years to media trust critical threshold	5-10	3-20	Low
Trust rebuilding time after major violation	10-20 years	5-50 years	Low
AI contribution to recent trust decline	10-25%	5-40%	Very Low

Key Insights

Asymmetry is fundamental - Trust erodes faster than it builds, making prevention crucial
Cascades are dangerous - Trust erosion in one domain spreads to others
Thresholds matter - Below certain levels, trust becomes self-reinforcing distrust
Rebuilding is generational - Severe trust violations may only heal across generations
AI accelerates existing trends - AI amplifies trust erosion mechanisms that existed before
Technical solutions insufficient - Rebuilding trust requires social and institutional change, not just technical fixes

Trust Cascade Model - Cascade dynamics in detail
Epistemic Collapse Threshold - Information trust failure
Deepfakes Authentication Crisis - Visual evidence trust

Sources

KPMG/University of Melbourne: Trust, Attitudes and Use of AI: A Global Study 2025 - Survey of 48,000+ people across 47 countries on AI trust and adoption
2025 Edelman Trust Barometer - 25th annual survey of 33,000+ respondents on institutional trust
Pew Research Center: How the US Public and AI Experts View Artificial Intelligence (2025) - Comparative analysis of public vs expert AI attitudes
Urban Institute: Understanding the Crisis in Institutional Trust (2024) - Academic analysis of trust erosion dynamics
Frontiers in Psychology: Trust Asymmetry Research (2023) - Research on asymmetric impacts of negative vs positive events on trust
Deloitte: Deepfake Disruption Report (2025) - Projections on AI fraud losses and deepfake proliferation
UNESCO: Deepfakes and the Crisis of Knowing - Analysis of synthetic media’s impact on shared reality

What links here

Societal Trustai-transition-model-parameteranalyzed-by
Institutional Qualityai-transition-model-parameteranalyzed-by

Trust Erosion Dynamics Model

Trust Erosion Dynamics Model

Overview

Trust Erosion Framework

Types of Trust Affected

Erosion Mechanisms

1. Deepfake Reality Distortion

2. AI-Enabled Disinformation

3. Surveillance Chilling Effects

4. Expert Displacement

5. Authentication Collapse

Model Parameters

Trust Dynamics Model

Building vs. Eroding Trust

The Trust Cascade

Quantitative Framework

Trust Threshold Analysis

Critical Trust Thresholds

Current Trust Levels (US Estimates)

Approach to Critical Thresholds

Scenario Analysis

Trust Rebuilding Challenges

Why Trust is Hard to Rebuild

Rebuilding Requirements

Intervention Strategies

Preventing Erosion

Slowing Erosion

Rebuilding Trust

Model Limitations

Uncertainty Ranges

Key Insights

Related Models

Sources

Related Pages

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