1. Executive Summary
Purpose. Investigate emotional fatigue signals in AI-driven outreach and detect automation distrust patterns.
2. Hypothesis & Theoretical Framework
Hypothesis. Certain outreach patterns cause “innovation fatigue”: overly automated tones reduce trust.
Theory Link. Emotional contagion and trust calibration — monotonous or synthetic affect reduces perceived sincerity.
Predicted Outcome. Negative sentiment correlates with higher automation pattern density.
Potential Impact. Guides tone modulation in future outreach models.
3. Experiment Execution Plan
- Source: synapse-lab submissions tagged “obstacle”.
- Sample Size: 5,000 messages.
- Analysis: Linear model: Sentiment = α − β · AutomationDensity
- Output: Monthly AI Outreach Health Index.
4. Results — Simulated September
| Metric | Value |
|---|---|
| Mean Sentiment | +0.14 |
| Mean Automation Density | 0.36 |
| Correlation (r) | −0.62 |
| Health Index | 0.78 |
5. Data Analysis & Interpretation Framework
- Raw Analysis: NLP sentiment + phrase repetition scoring.
- Feedback Loop: Calibrate models to lower automation density where sentiment dips.
- Peer Review: Validate human perception alignment.
- Integration: Feed the Health Index into scheduler limits.
6. Insights & Discussion
- “Even good automation can sound tired.”
- Messages with ≥ 0.5 automation density showed −0.3 sentiment delta.
- Human annotation confirms “AI fatigue” perception in repetitive structures.
- Recommendation: Insert micro-variance (syntax, pacing) into message pools.
7. Ethical Considerations
- All content anonymised and aggregated.
- Focus on communication tone, not individual users.
- Goal: preserve human authenticity within automation.
8. Contribution to the Synapse Ecosystem
- Data Improvement: Calibrates tone modulation layer in the outreach stack.
- Educational Value: Teaches members how “too smooth” feels synthetic.
- Innovation Signal: Enables a living AI Outreach Health Index for monthly monitoring.