Trust Erosion Dynamics Model

trust-erosion-dynamics (E363)

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societal-trust	Societal Trust	ai-transition-model-parameter	analyzed-by
institutional-quality	Institutional Quality	ai-transition-model-parameter	analyzed-by

Frontmatter

{
  "title": "Trust Erosion Dynamics Model",
  "description": "This model analyzes how AI systems erode institutional trust through deepfakes, disinformation, and authentication collapse. It finds trust erodes 3-10x faster than it builds, with only 46% of people globally willing to trust AI systems and US institutional trust at 18-30%, approaching critical governance failure thresholds.",
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Raw MDX Source

---
title: Trust Erosion Dynamics Model
description: This model analyzes how AI systems erode institutional trust through deepfakes, disinformation, and authentication collapse. It finds trust erodes 3-10x faster than it builds, with only 46% of people globally willing to trust AI systems and US institutional trust at 18-30%, approaching critical governance failure thresholds.
sidebar:
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quality: 59
lastEdited: "2026-01-28"
ratings:
  focus: 8.5
  novelty: 4
  rigor: 6.5
  completeness: 7
  concreteness: 6
  actionability: 4.5
importance: 56.5
update_frequency: 90
llmSummary: Analyzes how AI systems erode institutional trust through deepfakes, disinformation, and authentication collapse, finding trust erodes 3-10x faster than it builds, with US institutional trust at 18-30% approaching critical governance failure thresholds below 20%. Finds 46% global AI trust rate, 245% YoY deepfake misinformation growth, and 61% high-grievance population globally, with estimated 5-10 years to critical media trust threshold.
clusters:
  - ai-safety
  - epistemics
subcategory: societal-models
entityType: model
---
import {DataInfoBox, EntityLink, Mermaid} from '@components/wiki';

<DataInfoBox entityId="E363" ratings={frontmatter.ratings} />

## 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](https://kpmg.com/xx/en/our-insights/ai-and-technology/trust-attitudes-and-use-of-ai.html) 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](https://www.edelman.com/trust/2025/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.

<Mermaid chart={`
flowchart TD
    subgraph Mechanisms["AI Erosion Mechanisms"]
        DF[Deepfakes & Synthetic Media]
        DI[AI-Enabled Disinformation]
        SU[Surveillance & Monitoring]
        ED[Expert Displacement]
        AC[Authentication Collapse]
    end

    subgraph TrustDomains["Trust Domains"]
        IT[Institutional Trust]
        ET[Expert Trust]
        INF[Information Trust]
        IP[Interpersonal Trust]
        TT[Technology Trust]
    end

    subgraph Outcomes["Cascade Effects"]
        GF[Governance Failure]
        EF[Epistemic Fragmentation]
        SA[Social Atomization]
        SRD[Self-Reinforcing Distrust]
    end

    DF --> INF
    DF --> IT
    DI --> INF
    DI --> IT
    SU --> IT
    SU --> IP
    ED --> ET
    ED --> TT
    AC --> IP
    AC --> INF

    IT --> GF
    INF --> EF
    IP --> SA
    ET --> GF

    GF --> SRD
    EF --> SRD
    SA --> SRD

    SRD -.->|Feedback Loop| IT
    SRD -.->|Feedback Loop| IP
`} />

### 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](https://www.deloitte.com/us/en/insights/industry/technology/technology-media-and-telecom-predictions/2025/gen-ai-trust-standards.html) 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](https://kpmg.com/xx/en/our-insights/ai-and-technology/trust-attitudes-and-use-of-ai.html) |
| US AI trust rate | 32% | 25-40% | High | [Edelman 2025](https://www.edelman.com/trust/2025/trust-barometer) |
| Trust erosion/building asymmetry | 5x | 3-10x | Medium | [Trust asymmetry research](https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2023.1207453/full) |
| Deepfake misinformation growth (YoY) | 245% | 150-350% | Medium | [Deloitte 2024](https://www.deloitte.com/us/en/insights/industry/technology/technology-media-and-telecom-predictions/2025/gen-ai-trust-standards.html) |
| Public concern about AI (US) | 50% | 45-55% | High | [Pew Research 2025](https://www.pewresearch.org/internet/2025/04/03/how-the-us-public-and-ai-experts-view-artificial-intelligence/) |
| High grievance population (global) | 61% | 55-67% | High | [Edelman 2025](https://www.edelman.com/trust/2025/trust-barometer) |
| AI contribution to trust decline | 15-25% | 5-40% | Low | Model estimate |
| Detection accuracy for deepfakes | Below 60% | 40-65% | Medium | [iProov 2024](https://www.iproov.com/blog/deepfakes-statistics-solutions-biometric-protection) |

## 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](https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2023.1207453/full) 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](https://www.edelman.com/trust/2025/trust-barometer), [Pew Research 2025](https://www.pewresearch.org/internet/2025/04/03/how-the-us-public-and-ai-experts-view-artificial-intelligence/), 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](https://www.urban.org/sites/default/files/2024-04/Understanding_the_Crisis_in_Institutional_Trust.pdf) 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

1. **Asymmetry is fundamental** - Trust erodes faster than it builds, making prevention crucial

2. **Cascades are dangerous** - Trust erosion in one domain spreads to others

3. **Thresholds matter** - Below certain levels, trust becomes self-reinforcing distrust

4. **Rebuilding is generational** - Severe trust violations may only heal across generations

5. **AI accelerates existing trends** - AI amplifies trust erosion mechanisms that existed before

6. **Technical solutions insufficient** - Rebuilding trust requires social and institutional change, not just technical fixes

## Related Models

- <EntityLink id="E361" label="Trust Cascade Model" /> - Cascade dynamics in detail
- <EntityLink id="E120" label="Epistemic Collapse Threshold" /> - Information trust failure
- <EntityLink id="E97" label="Deepfakes Authentication Crisis" /> - Visual evidence trust

## Sources

- [KPMG/University of Melbourne: Trust, Attitudes and Use of AI: A Global Study 2025](https://kpmg.com/xx/en/our-insights/ai-and-technology/trust-attitudes-and-use-of-ai.html) - Survey of 48,000+ people across 47 countries on AI trust and adoption
- [2025 Edelman Trust Barometer](https://www.edelman.com/trust/2025/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)](https://www.pewresearch.org/internet/2025/04/03/how-the-us-public-and-ai-experts-view-artificial-intelligence/) - Comparative analysis of public vs expert AI attitudes
- [Urban Institute: Understanding the Crisis in Institutional Trust (2024)](https://www.urban.org/sites/default/files/2024-04/Understanding_the_Crisis_in_Institutional_Trust.pdf) - Academic analysis of trust erosion dynamics
- [Frontiers in Psychology: Trust Asymmetry Research (2023)](https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2023.1207453/full) - Research on asymmetric impacts of negative vs positive events on trust
- [Deloitte: Deepfake Disruption Report (2025)](https://www.deloitte.com/us/en/insights/industry/technology/technology-media-and-telecom-predictions/2025/gen-ai-trust-standards.html) - Projections on AI fraud losses and deepfake proliferation
- [UNESCO: Deepfakes and the Crisis of Knowing](https://www.unesco.org/en/articles/deepfakes-and-crisis-knowing) - Analysis of synthetic media's impact on shared reality