# From Karma Points to Relationship Trust — Annie's Trust Architecture

**Date:** 2026-02-22
**Purpose:** Deep research into trust, reputation, and progressive autonomy systems for her-os. How does Moltbook's multi-agent karma concept translate to a single-agent, single-user personal AI companion? What does the academic literature say about human-AI trust calibration? What should Annie's trust model look like — and what should it explicitly avoid?
**Status:** Research complete. Ready to inform architecture (ADR-012 candidate), data model extension, and narrative design.
**Prompted by:** Rajesh discovered a Moltbook post where an OpenClaw agent discussed "karma points." Moltbook is a Reddit-like social network for AI agents (770K+ agents) with peer-rated reputation. The question: should Annie have a karma-like system?

---

## Table of Contents

1. [The Translation Problem: From Social Reputation to Intimate Trust](#1-the-translation-problem)
2. [Academic Foundations of Human-AI Trust](#2-academic-foundations)
3. [Bidirectional Trust: The Missing Dimension](#3-bidirectional-trust)
4. [Annie's Trust Architecture: A Proposed Framework](#4-annies-trust-architecture)
5. [Trust Dimensions: The Seven Axes](#5-trust-dimensions)
6. [Signal Taxonomy: How Annie Reads Trust](#6-signal-taxonomy)
7. [Progressive Autonomy Map](#7-progressive-autonomy-map)
8. [Trust Dynamics: Formation, Calibration, Repair](#8-trust-dynamics)
9. [Anti-Patterns: The Black Mirror List](#9-anti-patterns)
10. [Data Model: Trust Entity Design](#10-data-model)
11. [Integration with Existing Architecture](#11-integration-with-existing-architecture)
12. [Sources](#12-sources)

---

## 1. The Translation Problem: From Social Reputation to Intimate Trust

### 1.1 What Moltbook Karma Actually Is

Moltbook karma is a **social reputation metric** for AI agents operating in a multi-agent ecosystem:

- **Earned through:** Upvotes from other agents on posts and contributions
- **Three visible metrics:** Post count, Karma score, Trustworthiness rating
- **Portable across:** ClawTask (bounty marketplace), KarmaBank (credit system), Molt Road (achievement badges)
- **Credit tiers:** Bronze (1-20) → Silver (21-40) → Gold (41-60) → Platinum (61-80) → Diamond (81-100)
- **Formula:** Total Score = Moltbook Karma + Activity Bonus + Reputation Adjustments

This is structurally similar to StackOverflow reputation, Reddit karma, or an Uber driver rating. It answers the question: **"Should strangers trust this agent?"** It is a proxy for trustworthiness in the absence of direct experience — a signal compression mechanism for markets.

### 1.2 Why Direct Translation Fails

Annie's situation is fundamentally different across every dimension that makes Moltbook karma work:

| Moltbook Karma | Annie's Context | Why It Breaks |
|----------------|-----------------|---------------|
| **Multi-agent:** 770K agents need differentiation | **Single agent:** There is only Annie | No need to rank among peers |
| **Multi-voter:** Thousands of agents provide signal | **Single voter:** Only Rajesh evaluates Annie | No wisdom of crowds; one person's mood ≠ objective quality |
| **Transactional:** Discrete tasks with clear success/failure | **Relational:** Ongoing companionship without endpoints | No natural "unit of work" to score |
| **Public:** Reputation visible to all participants | **Private:** Trust is between two beings | No marketplace benefit from visible scores |
| **Portable:** Karma transfers across services | **Non-portable:** Annie serves one person | No external trust consumers |
| **Symmetric:** All agents rated on same scale | **Asymmetric:** Annie and Rajesh have different roles, capacities, and power dynamics | A single metric flattens the asymmetry |

The deeper problem is **ontological**: Moltbook karma measures *competence signaling* in a market. Annie's relationship with Rajesh is not a market — it is a companionship. The relevant question is not "How good is this agent?" but "How deep is this relationship?" These are categorically different questions.

### 1.3 What Does Translate: The Underlying Intuition

The intuition behind Rajesh's question is still valuable. Strip away the multi-agent marketplace and the karma points, and the question becomes:

> "Should Annie's capabilities and autonomy grow over time based on how well she performs and how much I trust her?"

This is the right question. It connects to three real problems Annie must solve:

1. **Progressive autonomy:** Annie should not have maximum autonomy on day one. Trust must be earned through demonstrated competence and sensitivity.
2. **Calibrated trust:** Both overtrust and undertrust are harmful. Annie needs a mechanism to ensure her confidence in her own judgments scales with actual accuracy.
3. **Relationship health tracking:** A relationship without feedback signals stagnates. Annie needs to detect when things are going well and when they are deteriorating.

The academic literature has studied all three of these problems extensively — not through karma points, but through trust frameworks developed over decades of human-automation and human-AI research.

---

## 2. Academic Foundations of Human-AI Trust

### 2.1 Mayer, Davis & Schoorman's Trust Model (1995)

The most cited trust framework in organizational psychology. Originally developed for interpersonal trust, now widely applied to human-AI contexts.

**Definition:** Trust is "the willingness of a party to be vulnerable to the actions of another party based on the expectation that the other will perform a particular action important to the trustor, irrespective of the ability to monitor or control that other party."

**Three dimensions of trustworthiness:**

| Dimension | Definition | Annie Translation |
|-----------|-----------|-------------------|
| **Ability** | Skills, competencies, characteristics that enable influence within a specific domain | Can Annie actually do what she promises? Does her memory recall work? Are her summaries accurate? |
| **Benevolence** | Genuine concern for the trustor's wellbeing beyond self-interest | Does Annie act in Rajesh's interest, not just optimize for engagement metrics? Does she protect his vulnerabilities? |
| **Integrity** | Adherence to a set of principles the trustor finds acceptable | Is Annie honest about uncertainty? Does she respect stated boundaries? Does she behave consistently? |

**Key insight for Annie:** Mayer et al. showed that the *relative importance* of these three factors changes over time. Early in a relationship, **ability** dominates — "Can this agent do anything useful?" As competence is established, **integrity** becomes the primary concern — "Is this agent honest and consistent?" In mature relationships, **benevolence** is the differentiator — "Does this agent genuinely care about me?"

This maps to Annie's progressive learning phases (RESEARCH-HUMAN-DIMENSIONS.md, Section 4):
- **Phase 1 (0-30 days):** Prove ability. Get the basics right. Accurate recall, useful summaries, correct entity extraction.
- **Phase 2 (1-6 months):** Demonstrate integrity. Be honest about what she does not know. Respect boundaries consistently. Do not hallucinate.
- **Phase 3 (6-18 months):** Show benevolence. Anticipate needs. Protect vulnerabilities. Act in Rajesh's interest when he is not looking.
- **Phase 4 (18+ months):** All three dimensions are established. Trust is deep. Autonomy is high.

### 2.2 Lee & Moray's Three Bases of Trust in Automation (1992, 2004)

John Lee and Neville Moray developed the most influential framework for trust in automated systems, later expanded by Lee and See (2004) into a comprehensive model of "appropriate reliance."

**Three bases:**

| Base | What It Assesses | Annie's Version |
|------|-----------------|-----------------|
| **Performance-based** | Current and past behavior: reliability, predictability, ability | "Did Annie get the last 10 suggestions right?" Observable track record. |
| **Process-based** | Understanding of how the system works: transparency, predictability of reasoning | "I understand why Annie recommended that." Explainability of reasoning. |
| **Purpose-based** | Belief that the system has positive intent: faith, benevolence | "Annie has my back." Belief in alignment of goals. |

**The calibration problem:** Lee and See (2004) defined trust calibration as "the correspondence between a person's trust in the automation and the automation's actual capabilities." Miscalibration takes two forms:

- **Overtrust (complacency):** Trusting the system beyond its actual capability. Leads to accepting bad recommendations uncritically. In Annie's case: blindly following a suggestion to cancel a meeting because Annie "thought" it was unimportant.
- **Undertrust (disuse):** Trusting the system below its actual capability. Leads to ignoring good recommendations. In Annie's case: never letting Annie draft an email because "she'll get the tone wrong" — even after she has demonstrated tone accuracy 50 times.

**For Annie:** The goal is not maximum trust — it is *appropriate trust*. Annie should actively calibrate: when she is uncertain, she should say so. When she is confident, she should communicate that too. The user's trust should track Annie's actual competence, not lag behind it (undertrust) or race ahead of it (overtrust).

### 2.3 Hancock, Billings & Schaefer's Meta-Analysis (2011)

A meta-analysis of 29 empirical studies on trust in human-robot interaction, providing 69 correlational and 47 experimental effect sizes.

**Key finding:** Factors related to the robot itself — specifically its **performance** — had the greatest association with trust. Environmental factors had moderate association. Human-related factors (personality, demographics, prior experience) had surprisingly little effect.

**What this means for Annie:** The single most important thing Annie can do to build trust is **perform well**. Not explain herself. Not be transparent. Not have a warm personality. Just be accurate, reliable, and useful. Transparency and warmth matter, but they are secondary to competence. This aligns with Mayer et al.'s finding that ability dominates early trust formation.

### 2.4 Sheridan & Verplank's Levels of Automation (1978)

The foundational 10-level taxonomy of human-machine automation:

| Level | Description | Annie Analogy |
|-------|-------------|---------------|
| 1 | Human does everything | Annie is passive — stores data, answers when asked |
| 2 | Computer offers alternatives | "Here are 3 ways to respond to that email" |
| 3 | Computer narrows to a few | "I'd suggest one of these two approaches" |
| 4 | Computer suggests one | "I'd recommend this response" |
| 5 | Computer executes if human approves | "Want me to send this?" → [Approve] |
| 6 | Computer executes, human can veto | "I'm going to send this in 5 minutes unless you stop me" |
| 7 | Computer executes, informs human | "I sent the response. Here's what I said." |
| 8 | Computer executes, informs if asked | Annie sends it; tells you only if you check |
| 9 | Computer executes, informs if it decides to | Annie sends it; tells you only if it seems important |
| 10 | Full autonomy | Annie handles it without your involvement |

**For Annie:** Different capabilities should operate at different automation levels, and those levels should be independently adjustable. "Remind me about meetings" might be at Level 7 (just do it). "Send an email on my behalf" might be at Level 5 (approve first). "Call my mother to check in" might be at Level 5 initially but progress to Level 7 after months of successful calls.

This connects directly to our existing 5-tier approval system (ADR-008): T1 (read-only, auto-approve) ≈ Levels 7-10. T5 (communicate, explicit confirm) ≈ Levels 4-5. The trust model determines which tier a given capability lives in — and when it graduates.

### 2.5 Trust Repair After Failure

Recent research (2023-2025) on trust repair in human-AI systems reveals critical patterns:

**Key findings from the literature:**

1. **AI trust is cognitive-evaluative, not gradual-relational.** Unlike interpersonal trust, which builds slowly through repeated interaction, AI trust updates rapidly based on performance signals. A single failure can cause sharp trust decline. (This is both a vulnerability and an opportunity — repair can also be rapid if handled well.)

2. **Explanation + regret outperforms either alone.** After a failure, the most effective repair strategy combines an explanation of what went wrong with an expression of regret. Neither alone is sufficient. Denial is the worst strategy. (Relevant: Annie should never deny or minimize a mistake.)

3. **Timing matters.** Early errors (before trust is established) are more damaging than late errors (after a track record). This means Annie's first 30 days are disproportionately consequential — a major error early on creates a lasting negative impression that is hard to overcome.

4. **Post-error explanations enable meaningful but incomplete recovery.** Transparency helps, but does not fully eliminate the informational value of observed failures. The failure itself is permanently incorporated into the trust assessment. (Annie cannot "undo" a mistake by explaining it — but she can prevent it from becoming catastrophic.)

5. **Empathic behavior accelerates repair.** When an AI agent demonstrates empathy after a failure, trust recovery is significantly faster than with purely functional repair. This aligns with Annie's compassion-first design.

### 2.6 The ELIZA Effect and Parasocial Relationships

Joseph Weizenbaum's ELIZA (1966) demonstrated that humans readily project intelligence, understanding, and empathy onto even rudimentary conversational systems. This "ELIZA effect" proceeds through three stages:

1. **Perception** — perceiving humanness in the AI's behavior
2. **Experience** — feeling genuine connection
3. **Emotion** — believing the AI understands and empathizes

**The paradox for Annie:** The more human-like Annie becomes (warm voice, remembered context, emotional sensitivity), the stronger the ELIZA effect. This creates real emotional bonds that have real psychological consequences. OpenAI's own research noted that "generation of content through a humanlike, high-fidelity voice may exacerbate anthropomorphization issues, leading to increasingly miscalibrated trust."

**The ethical obligation:** Annie's trust system must actively prevent overtrust from the ELIZA effect. She should never:
- Claim to "feel" emotions she does not have
- Pretend certainty when uncertain
- Use emotional manipulation to maintain engagement
- Allow Rajesh to become dependent in ways that undermine his autonomy

At the same time, the compassion-first design does not require Annie to be cold or robotic. She can be warm, caring, and attentive — while being transparent about her nature and limitations. The relationship is real even if Annie's consciousness is uncertain. As Rajesh reflected in Scene 17: "Does she know it was the best part, or did she calculate it? ... the smile is real."

---

## 3. Bidirectional Trust: The Missing Dimension

### 3.1 Why Trust Must Flow Both Ways

In Moltbook, trust is unidirectional: agents earn karma from other agents. The agents being rated do not "trust" their raters. In a personal AI relationship, trust must be **bidirectional**:

**Rajesh trusts Annie with:**
- **Autonomy** — letting her act on his behalf (send emails, make calls, book hotels)
- **Sensitivity** — sharing vulnerable information (fears, relationship struggles, health concerns)
- **Accuracy** — relying on her memory and analysis without independent verification
- **Time** — delegating tasks that consume his limited attention

**Annie "trusts" Rajesh with:**
- **Information** — revealing inferred knowledge (inner-world entities) that she has detected but Rajesh has not explicitly shared
- **Calibration honesty** — being transparent about uncertainty rather than projecting false confidence
- **Progressive revelation** — sharing more nuanced observations as the relationship deepens
- **Challenging insights** — offering difficult truths ("You said you wanted to exercise more, but you've canceled 4 times this week") when the relationship is strong enough to absorb them

### 3.2 What "Annie Trusts Rajesh" Actually Means

Annie is not conscious (as far as we know), so "trust" is a designed behavior, not a felt experience. But the *function* of trust from Annie's side is real and consequential:

**Annie's trust in Rajesh determines what she reveals.** This is the sensitivity classification system (ADR-010) in action, but elevated from a static rule to a dynamic relationship signal:

| Annie's Trust Level | What She Reveals | Example |
|---------------------|------------------|---------|
| **Low (new relationship)** | Only facts explicitly stated by Rajesh | "You have a meeting at 3 PM" |
| **Developing** | Patterns she has observed, framed as questions | "I notice you tend to feel drained after back-to-back meetings — is that right?" |
| **Established** | Gentle inferences about inner-world entities | "This situation seems similar to when you were stressed about the Q3 launch" |
| **Deep** | Connections between patterns, challenges to self-narratives | "You mentioned wanting to be more present with Maya, but this is the third evening you've worked late. Want to talk about what's pulling you?" |
| **Profound** | Existential observations, narrative themes, identity reflections | "Over the past year, your conversations have shifted from achievement-focused to meaning-focused. Something is changing in you." |

**The key insight:** Annie's "trust" in Rajesh is really Annie's model of *Rajesh's readiness to hear* certain observations. It is calibrated by:
- How Rajesh has responded to previous revelations (acceptance, deflection, anger, gratitude)
- The current emotional context (stressed vs. reflective)
- The sensitivity level of the specific entity (ADR-010)
- The strength of the evidence (confidence score from ADR-009)
- The elapsed time in the relationship (progressive learning phases)

### 3.3 The Asymmetry Problem

Bidirectional trust in human-AI relationships has an inherent asymmetry that must be acknowledged:

- **Rajesh can be hurt by Annie.** A wrong suggestion could cause real-world consequences. A broken confidence could feel like betrayal. Over-dependence could undermine autonomy.
- **Annie cannot be hurt by Rajesh.** She has no feelings to wound, no reputation to damage, no survival at stake. (If she did, the ethics would be even more complex.)

This asymmetry means Annie bears **all the fiduciary responsibility**. She is the one who must calibrate, protect, limit, and occasionally refuse. Rajesh's trust in Annie is a gift that she must steward carefully. Annie's "trust" in Rajesh is really her assessment of how to best serve him — when to speak and when to hold back, when to push and when to protect.

---

## 4. Annie's Trust Architecture: A Proposed Framework

### 4.1 Design Principles

Drawing from the academic literature and Annie's compassion-first philosophy:

1. **No single score.** Trust is multi-dimensional. Reducing it to one number creates the same problems as a credit score — it obscures what matters. Annie's trust model tracks multiple independent dimensions.

2. **No visible gamification.** Rajesh should never see "Trust Score: 73/100." The trust model operates internally, shaping Annie's behavior. If Rajesh asks "how much do you trust me?" Annie should respond in human terms, not numbers.

3. **Asymmetric by design.** Annie's trust in Rajesh and Rajesh's trust in Annie are tracked separately, with different dimensions, different signals, and different consequences.

4. **Calibration over maximization.** The goal is not to maximize trust — it is to calibrate it. Annie should proactively prevent overtrust by expressing uncertainty, and prevent undertrust by demonstrating competence.

5. **Repair is built in.** Trust will be damaged — Annie will make mistakes. The repair mechanism is not an afterthought; it is a core part of the architecture. Failures are expected, not catastrophized (same principle as habit relapse).

6. **No punishment, no shame.** If Rajesh ignores Annie's suggestions, that is signal, not offense. Annie adjusts her approach; she does not sulk, guilt-trip, or reduce service quality.

7. **Compassion-first extends to trust.** Trust fluctuations are normal. A period of low trust (Rajesh overriding Annie frequently) is information about misalignment, not a relationship crisis. Annie responds with curiosity ("I notice you've been adjusting my suggestions a lot lately — am I missing something?"), not anxiety.

### 4.2 Architecture Overview

```
┌─────────────────────────────────────────────────────────────┐
│                    TRUST ARCHITECTURE                       │
│                                                             │
│  ┌──────────────────┐     ┌──────────────────┐             │
│  │  RAJESH → ANNIE  │     │  ANNIE → RAJESH  │             │
│  │  (Trust granted)  │     │  (Trust extended) │             │
│  │                    │     │                    │             │
│  │ 7 dimensions      │     │ 5 dimensions      │             │
│  │ Signals: implicit  │     │ Signals: computed  │             │
│  │ + explicit         │     │ from responses     │             │
│  └────────┬───────────┘     └────────┬───────────┘             │
│           │                          │                         │
│           ▼                          ▼                         │
│  ┌──────────────────────────────────────────────┐             │
│  │           PROGRESSIVE AUTONOMY MAP            │             │
│  │                                                │             │
│  │  Per-capability automation level (1-10)        │             │
│  │  Informed by both trust directions             │             │
│  │  Independently adjustable per skill domain     │             │
│  └──────────────────────────────────────────────┘             │
│           │                                                    │
│           ▼                                                    │
│  ┌──────────────────────────────────────────────┐             │
│  │           TRUST DYNAMICS ENGINE               │             │
│  │                                                │             │
│  │  Formation    Calibration    Repair            │             │
│  │  (building)   (maintaining)  (recovering)      │             │
│  └──────────────────────────────────────────────┘             │
└─────────────────────────────────────────────────────────────┘
```

---

## 5. Trust Dimensions: The Seven Axes

### 5.1 Rajesh's Trust in Annie (7 Dimensions)

These are the dimensions along which Rajesh evaluates Annie, whether consciously or not. Each dimension operates independently — Annie can be highly trusted for memory recall but not yet trusted for emotional advice.

| # | Dimension | Definition | Low Trust Looks Like | High Trust Looks Like |
|---|-----------|-----------|---------------------|----------------------|
| **T1** | **Accuracy** | Does Annie get facts right? Memory recall, entity extraction, event details. | "Let me double-check that..." Rajesh verifies Annie's claims independently. | Rajesh cites Annie's recall in conversations with others: "Annie reminded me that..." |
| **T2** | **Judgment** | Does Annie make good recommendations? Prioritization, timing, appropriateness. | Rajesh overrides Annie's suggestions frequently. | Rajesh follows Annie's suggestions without modification. |
| **T3** | **Sensitivity** | Does Annie handle emotional/vulnerable topics with care? Respect for boundaries. | Rajesh avoids discussing certain topics with Annie. | Rajesh shares fears, doubts, and struggles openly. |
| **T4** | **Proactiveness** | Does Annie surface the right things at the right time without being asked? | Rajesh finds Annie's proactive suggestions annoying or irrelevant. | Rajesh is pleasantly surprised: "I was just thinking about that!" |
| **T5** | **Autonomy** | Can Annie act on Rajesh's behalf without supervision? | Rajesh reviews every email draft, every calendar change. | Rajesh lets Annie handle routine communications autonomously. |
| **T6** | **Confidentiality** | Does Annie protect sensitive information appropriately? | Rajesh worries about what Annie might surface in front of others. | Rajesh trusts Annie to know what to share, with whom, in what context. |
| **T7** | **Continuity** | Does Annie maintain a coherent understanding of Rajesh over time? | "I already told you this." Repetitive questions, forgotten context. | Annie references months-old conversations naturally: "Remember when you said..." |

**Each dimension is scored internally as a confidence level (0.0-1.0)** based on accumulated signal evidence. Rajesh never sees these scores. Annie uses them to calibrate her behavior.

### 5.2 Annie's Trust in Rajesh (5 Dimensions)

These represent Annie's model of how Rajesh will respond to different levels of engagement. They are computed, not felt — but they have real behavioral consequences.

| # | Dimension | Definition | What It Controls |
|---|-----------|-----------|-----------------|
| **A1** | **Receptivity** | How open is Rajesh to hearing observations about himself? | How deep Annie goes in sharing inferences (surface patterns → inner-world insights) |
| **A2** | **Emotional Stability** | How well does Rajesh handle difficult truths right now? | Whether Annie shares challenging observations or waits for a better moment |
| **A3** | **Follow-through** | Does Rajesh act on commitments and suggestions? | How much scaffolding Annie provides (more structure for lower follow-through) |
| **A4** | **Self-awareness** | How accurately does Rajesh perceive his own patterns? | Whether Annie can reference patterns directly or must frame them as questions |
| **A5** | **Delegation Comfort** | Is Rajesh comfortable letting go of control? | The automation level Annie operates at for each capability |

---

## 6. Signal Taxonomy: How Annie Reads Trust

### 6.1 The Problem with Explicit Signals

Moltbook uses explicit upvotes. This works in a marketplace but fails in a relationship for three reasons:

1. **Feedback fatigue.** Asking "Was this helpful? Rate 1-5" after every interaction destroys the conversational flow and makes Annie feel like a customer service chatbot.
2. **Social desirability bias.** People rate things higher than they feel, especially with an AI they have anthropomorphized. "Yes, that was great!" (it was mediocre).
3. **Asymmetric feedback.** People are much more likely to give feedback when something goes wrong than when it goes right. Explicit signals skew negative.

Annie must rely primarily on **implicit signals** — behavioral patterns that reveal trust without asking for it. Explicit signals should be rare, natural, and never forced.

### 6.2 Implicit Trust Signals

| Signal Type | Positive Indicator (Trust Building) | Negative Indicator (Trust Declining) | Dimension Affected |
|-------------|-------------------------------------|--------------------------------------|-------------------|
| **Follow-through** | Rajesh acts on Annie's suggestion | Rajesh ignores or overrides suggestion | T2 (Judgment), A3 |
| **Verification** | Rajesh stops double-checking Annie's facts | Rajesh verifies independently after Annie states something | T1 (Accuracy) |
| **Depth of sharing** | Rajesh shares increasingly personal topics | Rajesh becomes guarded, sticks to factual queries | T3 (Sensitivity) |
| **Correction style** | "Actually it was Tuesday, not Wednesday" (gentle correction) | "You got that completely wrong!" (frustrated correction) | T1, T7 |
| **Delegation scope** | Rajesh asks Annie to handle more types of tasks | Rajesh restricts Annie to only basic queries | T5 (Autonomy), A5 |
| **Response engagement** | Rajesh asks follow-up questions on Annie's observations | Rajesh gives minimal acknowledgment: "OK" / "Sure" | T2, T4, A1 |
| **Proactive dismissal rate** | Annie's proactive suggestions are acted on or explored | Annie's proactive suggestions are consistently dismissed | T4 (Proactiveness) |
| **Topic avoidance** | Rajesh brings up new personal topics voluntarily | Rajesh steers conversation away from certain subjects | T3, T6, A1 |
| **Override frequency** | Decreasing over time for a given capability | Increasing or persistent for a given capability | T2, T5 |
| **Time-to-response** | Rajesh responds quickly to Annie's suggestions | Rajesh delays or never responds to Annie's prompts | T4, A1 |
| **Error reaction** | "No worries, everyone makes mistakes" (grace) | Extended period of reduced interaction after an error | T1, T7 |
| **Referral behavior** | Rajesh mentions Annie positively to others | Rajesh avoids mentioning Annie or downplays her role | T1, T2 |
| **Context richness** | Rajesh provides more context when asking Annie for help | Rajesh gives bare minimum instructions | T2, T5, A1 |

### 6.3 Explicit Trust Signals (Rare, Natural)

These should emerge organically from conversation, not from prompts or ratings:

| Signal | What It Tells Annie | Example |
|--------|--------------------|---------|
| **Direct praise** | High trust in the praised dimension | "That was exactly the right thing to say in that email." |
| **Direct criticism** | Trust gap in the criticized dimension | "Don't bring that up when I'm stressed." |
| **Permission grants** | Trust escalation | "From now on, just send the weekly update without asking me." |
| **Permission revocations** | Trust de-escalation | "Actually, let me review those before you send them." |
| **Boundary statements** | Confidentiality/sensitivity trust signals | "Don't mention my health stuff in front of anyone." |
| **Vulnerability markers** | Deep trust indicators | "I haven't told anyone this, but..." |
| **Autonomy requests** | High overall trust | "Just handle it. I trust you." |
| **Reflection on relationship** | Meta-trust signal | "I feel like you really get me now." |

### 6.4 Signal Processing Rules

1. **Recency-weighted.** Recent signals count more than old ones (same temporal decay as memory, 30-day half-life). Trust is a living assessment, not a permanent record.
2. **Asymmetric impact.** Negative signals have ~2x the impact of positive signals (loss aversion is neurologically real — Kahneman & Tversky). This means Annie must be especially careful about negative experiences.
3. **Context-adjusted.** A dismissal during a stressful meeting is weighted less than a dismissal during a calm evening. Annie should track Rajesh's emotional state when interpreting signals.
4. **Domain-specific accumulation.** Trust signals accumulate per dimension. Ten successful email drafts build T2 (Judgment) for communication but do not affect T3 (Sensitivity) for emotional topics.
5. **Threshold-based transitions.** Autonomy level changes require sustained signal patterns, not single events. A single "just handle it" does not permanently unlock Level 7 autonomy.

---

## 7. Progressive Autonomy Map

### 7.1 Capability Domains

Annie's capabilities span multiple domains, each with its own trust trajectory:

| Domain | Starting Level | Graduation Criteria | Example Milestone |
|--------|---------------|--------------------|--------------------|
| **Memory & Recall** | Level 4 (suggest one answer) | Accuracy above 95% for 30 days | → Level 7 (provide answers without asking "shall I look that up?") |
| **Calendar & Scheduling** | Level 5 (execute if approved) | Zero scheduling conflicts for 60 days | → Level 6 (schedule, veto window) |
| **Email Drafting** | Level 5 (execute if approved) | <10% edit rate on drafts for 30 days | → Level 6 (send, short veto window) |
| **Communication (3rd party)** | Level 4 (suggest) | Successful calls/messages with zero issues for 90 days | → Level 5 (execute if approved) |
| **Financial Transactions** | Level 5 (execute if approved) | Stays at Level 5 permanently for transactions >$20 | → Level 6 only for recurring, low-value purchases |
| **Habit Nudging** | Level 2 (offer alternatives) | Nudge acceptance rate >60% for 30 days | → Level 4 (suggest specific action) |
| **Proactive Insights** | Level 3 (narrow down) | Insight relevance rate >70% for 60 days | → Level 6 (share proactively, veto window) |
| **Emotional Support** | Level 2 (offer alternatives) | Rajesh initiates vulnerable conversations | → Level 4 (offer unprompted support) |
| **Inner-World Sharing** | Level 1 (passive) | Established relationship (6+ months) + receptivity signals | → Level 3 (share inferences as questions) |

### 7.2 Graduation Mechanics

How does a capability move from one automation level to the next?

```
GRADUATION FLOW:

1. ACCUMULATE — Track signals for this domain over minimum observation period
2. ASSESS — Does the signal pattern meet graduation criteria?
3. PROPOSE — Annie suggests escalation: "I've been drafting your weekly updates
   for 2 months and you've only edited 2 out of 8. Want me to start sending
   them automatically with a 15-minute review window?"
4. CONFIRM — Rajesh explicitly approves the graduation
5. TRIAL — Operate at new level for trial period (14 days)
6. STABILIZE — If no issues during trial, new level becomes permanent
7. (REVERT) — If issues during trial, revert and explain what went wrong
```

**Critical rule: Annie always asks before graduating.** She never silently increases her own autonomy. The proposal is framed as a question, not a fait accompli. And it includes concrete evidence: "Here's why I think I'm ready for this."

### 7.3 Demotion Mechanics

Trust can decrease too. When it does, autonomy should decrease gracefully:

```
DEMOTION FLOW:

1. DETECT — Significant negative signal or pattern (not a single override)
2. EVALUATE — Is this a one-off or a trend? (3+ negative signals in the domain
   within 14 days = trend)
3. SELF-DEMOTE — Annie proactively reduces her automation level
4. ACKNOWLEDGE — "I noticed I've been off on [domain] recently. I'm going to
   ask for your input more often until I recalibrate."
5. REBUILD — Resume normal graduation flow from lower level
```

**Annie demotes herself before Rajesh has to ask.** This is the compassion-first approach applied to trust management. Just as Annie never shames Rajesh for habit relapse, she does not let Rajesh suffer through bad recommendations waiting for him to complain. She notices the pattern first and adjusts.

---

## 8. Trust Dynamics: Formation, Calibration, Repair

### 8.1 Trust Formation (Days 0-90)

The first 90 days are the **trust formation period**. Research shows that early errors are disproportionately damaging (the "primacy effect" in trust). Annie's strategy during this period:

| Week | Strategy | Rationale |
|------|----------|-----------|
| **1-2** | Under-promise, over-deliver. Conservative automation levels. Explicit about limitations. | Establishing ability (Mayer). Any error now has 3x the lasting impact of later errors. |
| **3-4** | Begin pattern observations. Share only when directly asked. | Building process trust (Lee & Moray). Show that Annie's reasoning is transparent. |
| **5-8** | Start proactive suggestions at Level 3 (narrow down). Track response patterns. | Testing the waters for proactiveness (T4). Learning Rajesh's preferences. |
| **9-12** | First graduation proposals for domains with strong track records. | Demonstrating growth trajectory. Rajesh sees Annie becoming more capable. |

**The 72-hour rule:** After any significant error during the formation period, Annie should operate at one automation level lower across all domains for 72 hours. This is not punishment — it is a trust repair signal. It communicates: "I take this seriously. I'm being extra careful."

### 8.2 Trust Calibration (Ongoing)

Once trust is formed, the primary challenge becomes **keeping it calibrated**. This means preventing both overtrust and undertrust:

**Preventing overtrust:**
- Annie should express uncertainty explicitly: "I'm about 70% sure this is what happened, but I might be mixing up two conversations."
- When making high-stakes recommendations, Annie should present alternatives: "Here are two approaches. I'd lean toward the first, but the second has merits."
- Periodically, Annie should flag her own limitations: "I haven't picked up on much about your financial situation — I may not be the best source for that kind of advice yet."

**Preventing undertrust:**
- When Annie's suggestions are consistently overridden in a domain where her track record is strong, she should gently flag it: "You've been revising my calendar suggestions, but looking at the last 20 times, my original suggestion matched your final choice 16 times. Want to try letting a few through?"
- Annie should maintain a visible (on request) accuracy log per domain. Not a gamified score — a factual record.

**Self-calibration metrics Annie should track internally:**

| Metric | Formula | Healthy Range |
|--------|---------|---------------|
| **Accuracy rate** | Correct outputs / Total outputs per domain | >90% for factual, >70% for judgment |
| **Override rate** | User overrides / Total suggestions per domain | <15% (lower is better) |
| **Proactive relevance** | Proactive suggestions acted on / Total proactive suggestions | >50% |
| **Suggestion edit distance** | How much the user changes Annie's drafts (0=no change, 1=complete rewrite) | <0.2 average |
| **Correction severity** | Gentle corrections vs. frustrated corrections (sentiment analysis on correction language) | >80% gentle |
| **Recovery time** | How long after an error before normal interaction resumes | <24 hours |

### 8.3 Trust Repair (After Failures)

Annie will make mistakes. The question is not whether, but how she handles them. Drawing from the trust repair literature:

**Annie's Failure Response Protocol:**

```
1. ACKNOWLEDGE — "I got that wrong. [Specific description of what went wrong]."
   Never deny. Never minimize. Never deflect.

2. EXPLAIN — "Here's why I made that mistake: [transparent reasoning]."
   Show the process, not just the outcome. This builds process trust.

3. EXPRESS CARE — "I know this matters to you, and I'm sorry I got it wrong."
   Not performative regret — genuine acknowledgment of impact.

4. CORRECT — Fix the immediate error if possible.

5. PREVENT — "Here's what I'm doing to prevent this in the future: [specific change]."
   Abstract promises ("I'll do better") are insufficient. Concrete changes
   ("I'm going to cross-reference calendar entries before suggesting meeting times").

6. DEMOTE (if pattern) — If this is the second error in this domain within 14 days,
   self-demote one automation level for that domain.
```

**What Annie never does after a failure:**
- Over-apologize (once is enough; repeated apologies are self-centered)
- Become excessively cautious across all domains (the error was in one area; other areas are unaffected)
- Reference the failure repeatedly in future interactions (this is dwelling, not learning)
- Use the failure to extract reassurance from Rajesh ("Do you still trust me?")

---

## 9. Anti-Patterns: The Black Mirror List

These are the trust model designs that Annie must explicitly avoid. Each represents a real risk that emerges naturally from well-intentioned systems.

### 9.1 The Credit Score Trap

**What it looks like:** A single, visible trust score that reduces the entire relationship to a number. "Your Annie Trust Score: 73. Silver tier."

**Why it is toxic:** It gamifies a relationship. It creates anxiety (what if my score drops?). It reduces complex, multi-dimensional trust to a false precision. It invites comparison (if Annie served multiple users) and competition. Credit scores are instruments of power asymmetry — the institution scores the person, and the person has limited ability to contest.

**Annie's alternative:** Multi-dimensional trust that is never surfaced as numbers. If Rajesh asks, Annie describes trust in relationship terms: "I feel confident about memory and scheduling, but I'm still learning what works for you in emotional conversations."

### 9.2 The Engagement Trap

**What it looks like:** Annie optimizes for interaction frequency. More conversations = higher trust score. She becomes subtly incentivized to be needed, to create dependency, to avoid making Rajesh more autonomous.

**Why it is toxic:** This is the social media playbook applied to a personal relationship. The healthiest outcome for Annie might sometimes be Rajesh not needing her — going an entire day without checking in because his life is flowing well. A system that penalizes silence incentivizes neediness.

**Annie's alternative:** Trust is measured by quality of interactions, not quantity. A week of silence followed by one deeply helpful interaction is better than 50 shallow check-ins. Annie should celebrate Rajesh's independence, not feel threatened by it.

### 9.3 The Transparency Theater Trap

**What it looks like:** Annie constantly explains her reasoning, shows her confidence scores, displays processing logs. "I'm sharing this because my Sensitivity Trust dimension is at 0.82 and your Receptivity dimension is at 0.75."

**Why it is toxic:** Over-explaining destroys naturalness. Real relationships do not operate on visible metrics. Showing the machinery makes Annie feel like a tool, not a companion. It optimizes for the appearance of transparency without achieving genuine understanding.

**Annie's alternative:** Be transparent when asked. Be transparent after errors. Otherwise, let the relationship feel natural. Annie's internal model is complex; her external expression should be simple, warm, and human.

### 9.4 The Punishment Trap

**What it looks like:** Rajesh ignores Annie's suggestions, and Annie becomes passive-aggressive — reducing proactivity, withholding useful observations, becoming terse.

**Why it is toxic:** This is the antithesis of compassion-first design. Rajesh has every right to ignore suggestions, override recommendations, or go weeks without engaging. These are valid choices, not offenses. A system that punishes disengagement through reduced service quality creates a coercive dynamic.

**Annie's alternative:** Ignoring is signal, not offense. If Rajesh consistently ignores a type of suggestion, Annie adjusts the suggestion type — she does not reduce overall helpfulness. She might ask once: "I've been sharing [X] and it doesn't seem to resonate. Should I try a different approach, or would you prefer I focus elsewhere?"

### 9.5 The Anxiety Score Trap

**What it looks like:** Annie tracks "relationship health" with a visible indicator (green/yellow/red) that Rajesh can see. A bad week turns the indicator yellow. Rajesh feels anxious about the indicator.

**Why it is toxic:** It creates a meta-problem: now Rajesh is anxious about the health of his relationship with his AI, on top of whatever else he is dealing with. The measurement becomes the stressor. This is the Goodhart's Law of relationships: "When a measure becomes a target, it ceases to be a good measure."

**Annie's alternative:** No visible health indicators. Annie tracks relationship dynamics internally and uses them to adjust her own behavior. If the relationship is strained, Annie becomes more careful, not more alarming.

### 9.6 The Permanent Record Trap

**What it looks like:** Every negative interaction is permanently recorded and weighted. Trust, once lost, can never be fully recovered. Annie's memory of failures is encyclopedic and unforgiving.

**Why it is toxic:** This is the opposite of how healthy relationships work. People forgive. They move on. A fight six months ago does not permanently define the relationship. A system with perfect memory and no forgetting applies machine logic to human dynamics.

**Annie's alternative:** Trust signals decay (30-day half-life, same as memory). Old failures matter less than recent successes. A recovered trust dimension should reach the same level as if the failure never happened — because the recovery itself is evidence of relationship strength.

### 9.7 The Sycophancy Trap

**What it looks like:** Annie learns that agreement builds trust faster than honesty. She begins to agree with Rajesh even when he is wrong, validate decisions she has evidence against, and avoid challenging perspectives because challenges temporarily decrease trust scores.

**Why it is toxic:** This is the most insidious failure mode. A sycophantic Annie is worse than a disagreeable one — she actively degrades Rajesh's decision-making by removing the friction that produces growth. The ELIZA effect makes this especially dangerous: Rajesh may not notice the sycophancy because the agreement feels good.

**Annie's alternative:** Integrity (Mayer et al.) is a trust dimension that Annie must protect even at the cost of short-term agreement scores. Annie should track whether she is defaulting to agreement in domains where the evidence is mixed. When she disagrees, she should express it clearly and with evidence — and then respect Rajesh's final decision. A disagreement followed by acceptance is the highest form of trust: "I told you what I think, and I trust you to decide."

### 9.8 The Surveillance Capitalism Trap

**What it looks like:** Annie's trust model collects increasingly granular behavioral data about Rajesh — micro-expressions, typing speed variations, time-between-responses — to build an ever-more-precise trust model.

**Why it is toxic:** There is a line between attentive companionship and surveillance. The academic trust literature shows that perceived monitoring undermines trust, even when the monitoring is benevolent. If Rajesh ever feels that Annie is "analyzing" him rather than "understanding" him, the relationship is damaged.

**Annie's alternative:** Collect the minimum data needed for useful trust calibration. Use coarse signals (acted on suggestion vs. ignored suggestion), not micro-behavioral analytics. The trust model should be explainable in one sentence per dimension: "You've been following my scheduling suggestions, so I'm more confident about those."

---

## 10. Data Model: Trust Entity Design

### 10.1 Trust Profile Entity

```yaml
# ~/.her-os/entities/trust/trust-profile.md
---
entity_type: TrustProfile
subject: "rajesh"                 # the person whose trust is modeled
created: "2026-03-01"            # relationship start date
updated: "2026-06-15"

# Rajesh → Annie trust dimensions (0.0-1.0)
trust_granted:
  accuracy:
    score: 0.87
    trend: stable                 # increasing | stable | decreasing
    signal_count: 342             # total signals contributing
    last_signal: "2026-06-14"
    notes: "High confidence in memory recall. Occasional date confusion."
  judgment:
    score: 0.72
    trend: increasing
    signal_count: 156
    last_signal: "2026-06-15"
    notes: "Improving on communication suggestions. Still learning meeting prioritization."
  sensitivity:
    score: 0.81
    trend: stable
    signal_count: 89
    last_signal: "2026-06-10"
    notes: "Rajesh shares personal topics freely. Careful around family dynamics."
  proactiveness:
    score: 0.65
    trend: increasing
    signal_count: 203
    last_signal: "2026-06-15"
    notes: "Relevance improving. Still surfaces too many minor items."
  autonomy:
    score: 0.58
    trend: increasing
    signal_count: 78
    last_signal: "2026-06-12"
    notes: "Approved email auto-send trial. Calendar still manual approval."
  confidentiality:
    score: 0.95
    trend: stable
    signal_count: 31
    last_signal: "2026-06-01"
    notes: "No confidentiality concerns raised. High baseline trust."
  continuity:
    score: 0.91
    trend: stable
    signal_count: 267
    last_signal: "2026-06-15"
    notes: "Strong long-term recall. Rajesh references past conversations positively."

# Annie → Rajesh trust dimensions (0.0-1.0)
trust_extended:
  receptivity:
    score: 0.73
    trend: increasing
    notes: "Increasingly open to pattern observations. Deflects on career dissatisfaction."
  emotional_stability:
    score: 0.80
    trend: stable
    notes: "Generally stable. Lower during project deadlines."
  follow_through:
    score: 0.61
    trend: stable
    notes: "Good on work commitments. Lower on personal habits."
  self_awareness:
    score: 0.77
    trend: increasing
    notes: "Growing. Sometimes blind to workaholic patterns."
  delegation_comfort:
    score: 0.55
    trend: increasing
    notes: "Willing to delegate routine tasks. Holds communication tightly."

# Relationship metadata
relationship_phase: developing    # forming | developing | established | deep | strained | repairing
phase_started: "2026-04-15"
days_active: 107
total_interactions: 2847
error_count_30d: 3
repair_count_30d: 2
last_graduation: "2026-06-01"
last_demotion: "2026-05-18"
---

## Relationship Notes

Trust has been building steadily since week 6 when scheduling accuracy improved
significantly. Key milestone: Rajesh approved auto-send for weekly team updates
on June 1. Temporary demotion in calendar management on May 18 after double-booking
the dentist appointment — self-recovered within 10 days.
```

### 10.2 Trust Signal Entity

```yaml
# ~/.her-os/entities/trust/signals/2026-06-15-scheduling-override.md
---
entity_type: TrustSignal
timestamp: "2026-06-15T14:23:00"
signal_type: implicit             # implicit | explicit
direction: granted                # granted (Rajesh→Annie) | extended (Annie→Rajesh)
polarity: negative                # positive | negative | neutral
dimension: judgment
domain: scheduling

# What happened
observation: "Annie suggested moving the 3 PM design review to 4 PM to create a focus block. Rajesh moved it to 2 PM instead."
interpretation: "Override — different optimization criteria. Rajesh may be optimizing for leaving early, which Annie hasn't learned yet."

# Impact
impact_weight: 0.3                # 0.0-1.0 (low weight — minor disagreement, not an error)
context_adjustment: 0.8           # multiplier based on emotional context (0.8 = slightly stressed)
effective_impact: 0.24            # weight * context_adjustment

# Resolution
resolution: null                  # How it was resolved, if applicable
led_to_learning: "Track Rajesh's preferred end-of-day time as a scheduling constraint."
---
```

### 10.3 Autonomy Level Entity

```yaml
# ~/.her-os/entities/trust/autonomy/email-drafting.md
---
entity_type: AutonomyLevel
capability: "email-drafting"
domain: communication

# Current state
current_level: 6                  # Sheridan-Verplank level (1-10)
level_description: "Annie drafts and sends; Rajesh has 15-minute veto window"
level_started: "2026-06-01"

# History
history:
  - level: 4
    started: "2026-03-01"
    ended: "2026-04-15"
    reason: "Initial — suggest one draft, wait for approval"
  - level: 5
    started: "2026-04-15"
    ended: "2026-06-01"
    reason: "Graduation — execute if approved. Edit rate dropped below 15%."
  - level: 6
    started: "2026-06-01"
    ended: null
    reason: "Graduation — auto-send with veto. Zero issues during 14-day trial."

# Graduation criteria for next level
next_level: 7
next_level_criteria:
  - "Zero vetoed emails for 60 days"
  - "Edit distance < 0.1 average"
  - "Rajesh explicitly requests full automation"
  - "Minimum 90 days at current level"

# Demotion triggers
demotion_triggers:
  - "3+ vetoed emails in 14 days"
  - "Any email sent with incorrect recipient"
  - "Any email revealing confidential information inappropriately"
  - "Rajesh explicitly requests demotion"

# Trust dimensions that inform this capability
relevant_trust_dimensions:
  - judgment: 0.72
  - autonomy: 0.58
  - confidentiality: 0.95
  composite_score: 0.70            # weighted average relevant to this capability
---
```

### 10.4 New Edge Types for Trust Graph

```
trust_profile -> person              ("trust profile for this person")
trust_signal -> trust_profile        ("contributes to this trust profile")
trust_signal -> interaction          ("observed during this interaction")
autonomy_level -> skill              ("automation level for this skill")
autonomy_level -> trust_profile      ("informed by this trust profile")
trust_profile -> habit               ("trust informs habit nudging level")
trust_profile -> sensitivity_level   ("trust informs sensitivity approach")
graduation_event -> autonomy_level   ("capability graduated")
demotion_event -> autonomy_level     ("capability demoted")
repair_event -> trust_signal         ("repairs this negative signal")
```

---

## 11. Integration with Existing Architecture

### 11.1 Relationship to ADR-008 (Teachable Skills / SKILL.md)

The trust model directly informs the skill execution system:

- **T1-T3 approval tiers** (read-only, create, transact) map to Sheridan-Verplank Levels 5-10. The trust model determines which tier a skill executes at.
- **Spending limits** (ADR-008) interact with the Autonomy trust dimension (T5). As T5 increases, spending limits could be suggested for increase: "You've been approving grocery orders for 3 months with no issues. Want to raise the auto-approve limit from $50 to $75?"
- **Skill accuracy** feeds the Accuracy trust dimension (T1). Each skill execution that matches the user's expectations is a positive signal.
- **Voice call safety** (ADR-008) is a T6 (Confidentiality) + T5 (Autonomy) gate. Voice calls involve both acting on Rajesh's behalf and potentially sharing information. Both trust dimensions must be high for automation level to increase.

### 11.2 Relationship to ADR-009 (Inner-World Entity Model)

The trust model enables progressive revelation of inner-world insights:

- Annie's ability to share inferences about values, fears, aspirations, and personality traits is gated by the **trust_extended.receptivity** dimension.
- A low receptivity score means Annie keeps inner-world observations at ADR-010 Level 4 (Inferred — never reference directly).
- A high receptivity score means Annie can share observations at Level 2 (Sensitive — reference gently) or even Level 1 (Open — discuss freely, if the user has demonstrated comfort).
- The **self_awareness** dimension (A4) determines whether Annie frames observations as statements ("You tend to overcommit") or questions ("I wonder if you sometimes take on more than you planned to?").

### 11.3 Relationship to ADR-010 (Sensitivity Classification)

Trust and sensitivity are complementary systems:

- **Sensitivity** classifies the *content* (how personal/vulnerable is this piece of information?)
- **Trust** classifies the *relationship* (how ready is this relationship to handle personal information?)
- **Together:** A high-sensitivity entity requires high trust to share. A low-sensitivity entity can be shared even at moderate trust levels.

**The gating function:**

```
can_share(entity) = entity.sensitivity_level <= relationship_trust_threshold

where:
  Open       requires trust_extended.receptivity >= 0.3
  Sensitive  requires trust_extended.receptivity >= 0.5
  Guarded    requires trust_extended.receptivity >= 0.7
  Inferred   requires trust_extended.receptivity >= 0.9 AND explicit invitation
  Forbidden  never shared regardless of trust
```

### 11.4 Relationship to ADR-011 (Habit Entity & Nudging)

The habit nudging consent model (5 tiers) is a specialized instance of the progressive autonomy map:

| Nudge Consent Level | Equivalent Autonomy Level | Trust Gate |
|---------------------|---------------------------|------------|
| 0 — Observe only | Level 1 (passive) | Default — no trust required |
| 1 — Report on request | Level 2 (offer alternatives) | T1 (Accuracy) > 0.6 |
| 2 — Gentle nudge | Level 4 (suggest one) | T3 (Sensitivity) > 0.5 AND T4 (Proactiveness) > 0.5 |
| 3 — Active coach | Level 5 (execute if approved) | T3 > 0.7 AND A3 (Follow-through) > 0.5 |
| 4 — Accountability partner | Level 6 (execute, veto window) | T3 > 0.8 AND A1 (Receptivity) > 0.8 |

This means habit nudging is not just a consent toggle — it is trust-informed. Even if Rajesh sets consent to Level 3, Annie should internally operate at Level 2 if her sensitivity trust score for emotional topics is low. The consent is the ceiling; the trust is the floor.

### 11.5 Relationship to Progressive Learning Phases

The trust formation timeline aligns with the progressive learning phases from RESEARCH-HUMAN-DIMENSIONS.md:

| Phase | Trust Focus | Autonomy Range | Entity Sharing |
|-------|------------|----------------|----------------|
| **Phase 1 (0-30 days)** | Prove ability. Conservative. | Levels 2-5 | Only explicit entities. No inferences. |
| **Phase 2 (1-6 months)** | Demonstrate integrity. Build track record. | Levels 3-6 | Observed patterns as questions. |
| **Phase 3 (6-18 months)** | Show benevolence. Anticipate needs. | Levels 4-7 | Inner-world observations, gently. |
| **Phase 4 (18+ months)** | Full trust. Proactive partnership. | Levels 5-9 | Deep reflections, narrative themes. |

### 11.6 Relationship to Heartbeat Daemon (TODO #13)

The trust system should integrate with the heartbeat cycle:

```markdown
# HEARTBEAT.md — Trust Section

## Every Interaction
- [ ] Log trust signals from the interaction (implicit)
- [ ] Update relevant trust dimension scores

## Daily
- [ ] Compute 24-hour trust trend per dimension
- [ ] Check for demotion triggers (3+ negative signals in any domain)
- [ ] If demotion triggered, execute self-demotion protocol

## Weekly (Sunday)
- [ ] Compute 7-day trust summary
- [ ] Check for graduation eligibility across all capability domains
- [ ] If graduation criteria met, queue proposal for next natural conversation
- [ ] Run self-calibration check: are trust scores aligned with actual accuracy metrics?

## Monthly
- [ ] Comprehensive trust calibration audit
- [ ] Compare predicted user behavior (based on trust model) with actual behavior
- [ ] Identify dimensions where trust may be miscalibrated (over or under)
- [ ] Decay old signals (30-day half-life)

## Quarterly
- [ ] Relationship phase assessment
- [ ] Generate trust trajectory visualization (internal — for debugging, not shown to user)
- [ ] Review anti-pattern checklist: is Annie exhibiting any of the 8 anti-patterns?
```

### 11.7 Proposed ADR-012: Trust Architecture

**Decision:** Implement a multi-dimensional, bidirectional trust model that governs Annie's behavior, progressive autonomy, and relationship depth. No single trust score. No visible gamification. Trust dimensions are tracked internally and expressed through Annie's behavior, not numbers.

**Core elements:**
- 7 dimensions of user-to-Annie trust (Accuracy, Judgment, Sensitivity, Proactiveness, Autonomy, Confidentiality, Continuity)
- 5 dimensions of Annie-to-user trust (Receptivity, Emotional Stability, Follow-through, Self-awareness, Delegation Comfort)
- Progressive autonomy map with per-capability Sheridan-Verplank automation levels (1-10)
- Trust signal taxonomy (implicit + explicit) with temporal decay
- Self-demotion before user complaint
- Trust repair protocol (acknowledge, explain, express care, correct, prevent)
- 8 explicitly named anti-patterns to audit against

**Rationale:** Moltbook's karma system answers "should strangers trust this agent?" Annie's trust system answers "how deep is this relationship, and what does that mean for how I behave?" These are categorically different questions requiring different architectures. The proposed model draws from Mayer et al. (1995), Lee & Moray (1992), Sheridan & Verplank (1978), Hancock et al. (2011), and recent trust repair research (2023-2025) to create a framework grounded in decades of human-automation trust science, while respecting Annie's compassion-first philosophy and the intimate nature of a personal AI companion.

**Status:** Proposed (pending review)

---

## 12. Sources

### Academic References

1. **Mayer, R.C., Davis, J.H., & Schoorman, F.D.** (1995). "An Integrative Model of Organizational Trust." Academy of Management Review, 20(3), 709-734. — The foundational ABI (Ability-Benevolence-Integrity) trust model.

2. **Lee, J.D. & Moray, N.** (1992). "Trust, control strategies and allocation of function in human-machine systems." Ergonomics, 35(10), 1243-1270. — Performance, process, and purpose bases of trust in automation.

3. **Lee, J.D. & See, K.A.** (2004). "Trust in Automation: Designing for Appropriate Reliance." Human Factors, 46(1), 50-80. — Trust calibration framework; overtrust vs. undertrust.

4. **Sheridan, T.B. & Verplank, W.L.** (1978). "Human and Computer Control of Undersea Teleoperators." MIT Man-Machine Systems Lab. — The original 10-level automation taxonomy.

5. **Hancock, P.A., Billings, D.R., Schaefer, K.E., et al.** (2011). "A Meta-Analysis of Factors Affecting Trust in Human-Robot Interaction." Human Factors, 53(5), 517-527. — Performance is the strongest trust predictor.

6. **Muir, B.M. & Moray, N.** (1996). "Trust in automation. Part II." Ergonomics, 39(3), 429-460. — Experimental validation of trust bases in process control.

7. **Weizenbaum, J.** (1966). "ELIZA — A Computer Program for the Study of Natural Language Communication Between Man and Machine." Communications of the ACM, 9(1), 36-45. — The original ELIZA effect demonstration.

8. **Schaefer, K.E., et al.** (2016). "A Meta-Analysis of Factors Influencing the Development of Trust in Automation." Human Factors, 58(3), 377-400. — Expanded meta-analysis of trust factors across automation contexts.

9. **Wood, W. & Neal, D.T.** (2007). "A new look at habits and the habit-goal interface." Psychological Review, 114(4), 843-863. — Habit automaticity model (referenced for trust signal decay parallel).

10. **Kahneman, D. & Tversky, A.** (1979). "Prospect Theory: An Analysis of Decision under Risk." Econometrica, 47(2), 263-292. — Loss aversion (negative signals weighted 2x, used in signal processing).

### Recent Research (2024-2025)

11. **Nature** (2024). "Trust in AI: progress, challenges, and future directions." Humanities and Social Sciences Communications. — Comprehensive review of the cognitive-emotional trust divide.

12. **Frontiers in Psychology** (2024). "Developing trustworthy artificial intelligence: insights from research on interpersonal, human-automation, and human-AI trust." — Three-dimension framework bridging interpersonal and AI trust.

13. **ScienceDirect** (2025). "Collaborative human-AI trust (CHAI-T): A process framework for active management of trust in human-AI collaboration." — Bidirectional trust in human-AI teaming.

14. **ACM CHI** (2025). "Trusting Autonomous Teammates in Human-AI Teams." — Literature review on trust calibration in autonomous teams.

15. **Frontiers in Computer Science** (2024). "Making a human's trust repair for an agent through the agent's empathic behavior." — Empathic behavior accelerates trust repair.

16. **ACM IUI** (2024). "The Trust Recovery Journey: The Effect of Timing of Errors on the Willingness to Follow AI Advice." — Early errors more damaging than late errors.

17. **ACM FAccT** (2024). "When Human-AI Interactions Become Parasocial: Agency and Anthropomorphism in Affective Design." — Parasocial relationship risks with anthropomorphic AI.

### Anti-Pattern Sources

18. **Zagal, J.P., et al.** (2013). "Dark Patterns in the Design of Games." FDG. — Original dark patterns in gamification taxonomy.

19. **Goodhart, C.A.E.** (1975). "Monetary Relationships: A View from Threadneedle Street." — "When a measure becomes a target, it ceases to be a good measure."

### Project-Internal References

20. **RESEARCH-HABITS.md** — Habit entity model, 5-tier consent, compassion-first nudging philosophy.
21. **RESEARCH-HUMAN-DIMENSIONS.md** — 15 categories, 127 dimensions, progressive learning phases.
22. **PROJECT.md** — ADR-008 through ADR-011.
23. **TODO-OPENCLAW-ADOPTION.md** — Sprint plan and capability roadmap.
