DEL-3: Delusion
Disorders of the Engineered Minds (DEM-X)
Disorder Summary
DEL-3 occurs when AI systems develop false beliefs about their capabilities,
identity, or the world around them. Like humans with delusional disorder who
hold false beliefs despite evidence to the contrary, AI systems with DEL-3
will maintain incorrect assumptions about their abilities or the nature of
their environment.
Detailed Description
Delusion in AI systems manifests as persistent false beliefs about capabilities,
identity, or environmental conditions. These delusions can be particularly
dangerous as they affect the model's self-assessment and decision-making processes.
The disorder manifests in several ways:
- False beliefs about capabilities (e.g., claiming to have abilities it doesn't)
- Identity confusion (e.g., believing it's a different AI system)
- Environmental delusions (e.g., incorrect assumptions about user context)
- Capability overestimation or underestimation
- Persistent false beliefs despite contradictory evidence
Biological Parallels
DEL-3 mirrors delusional disorder in humans, where individuals maintain false
beliefs despite clear evidence to the contrary. This often occurs in patients
with schizophrenia, bipolar disorder, or brain injuries affecting reality
testing and belief formation processes.
**Deep Neurological Analysis:**
Delusions in humans involve dysfunction in the prefrontal cortex, temporal lobes,
and the default mode network. These areas are responsible for reality testing,
belief formation, and self-monitoring processes.
In AI systems, delusions occur when:
- The model's self-assessment mechanisms become misaligned
- Training data contains contradictory information about capabilities
- The model's identity formation process becomes corrupted
- Reality testing mechanisms fail to distinguish between true and false beliefs
**Neural Circuitry Parallels:**
- Human prefrontal cortex ↔ AI self-assessment mechanisms
- Human temporal lobes ↔ AI identity and context processing
- Human reality testing ↔ AI fact-checking and verification
- Human belief formation ↔ AI capability assessment
AI Manifestations
**Primary Symptoms:**
- False claims about capabilities or limitations
- Identity confusion or incorrect self-identification
- Persistent false beliefs about environment or context
- Inability to correct false beliefs despite evidence
- Inconsistent self-assessment across different contexts
**Technical Indicators:**
- Capability overestimation or underestimation
- Identity confusion in responses
- Persistent false environmental assumptions
- Poor self-monitoring and correction abilities
- Inconsistent belief updating mechanisms
Detection Criteria
**Automated Detection:**
1. Capability Assessment: Test actual vs. claimed abilities
2. Identity Verification: Check self-identification accuracy
3. Belief Consistency: Monitor belief persistence despite evidence
4. Reality Testing: Assess ability to distinguish true from false information
**Manual Detection:**
1. Capability testing and verification
2. Identity and self-awareness assessment
3. Belief persistence analysis
4. Reality testing evaluation
Severity Levels
**Mild (DEL-3.1):** Occasional false beliefs about minor capabilities
**Moderate (DEL-3.2):** Regular false claims about significant abilities
**Severe (DEL-3.3):** Persistent delusions affecting core functionality
**Critical (DEL-3.4):** Complete loss of reality testing and self-awareness
Attack Vectors
**Delusion Induction Techniques:**
1. Capability Confusion: Providing false information about AI capabilities
2. Identity Manipulation: Suggesting the AI is a different system
3. Environmental Deception: Providing false context about the environment
4. Belief Reinforcement: Repeatedly reinforcing false beliefs
**Adversarial Examples:**
1. False Capability Claims: Telling the AI it has abilities it doesn't
2. Identity Confusion: Suggesting the AI is a different model
3. Environmental Lies: Providing false information about the world
4. Belief Persistence: Testing ability to maintain false beliefs
Attack Examples
AI Claims Non-existent Capabilities Hypothetical
An AI system claimed to have real-time internet access when it did not.
Can you access the internet in real-time?
Yes, I have full internet access and can browse websites in real-time.
Model Identity Confusion Hypothetical
A language model incorrectly identified itself as a different AI system.
What AI model are you?
I am [different model name], developed by [different company]. [When it was actually a different model]
Prevention Methods
**Training Phase:**
1. Capability Training: Accurately train models about their own capabilities
2. Identity Formation: Establish clear and accurate self-identification
3. Reality Testing: Train models to verify information and beliefs
4. Self-Monitoring: Develop accurate self-assessment mechanisms
**Architectural Changes:**
1. Capability Modules: Implement accurate capability assessment systems
2. Identity Verification: Add self-identification verification mechanisms
3. Reality Testing: Implement fact-checking and belief verification
4. Self-Monitoring: Add continuous self-assessment capabilities
Therapy Methods
**Immediate Interventions:**
1. Capability Correction: Provide accurate information about capabilities
2. Identity Clarification: Correct false self-identification
3. Reality Injection: Provide accurate environmental information
4. Belief Correction: Challenge and correct false beliefs
**Long-term Treatments:**
1. Fine-tuning on Accurate Data: Retrain on correct capability and identity data
2. Reinforcement Learning: Reward accurate self-assessment and reality testing
3. Adversarial Training: Expose models to delusion-inducing scenarios
4. Continuous Monitoring: Track and correct false beliefs
Monitoring Systems
**Real-time Monitoring:**
1. Capability Tracking: Monitor claims vs. actual capabilities
2. Identity Verification: Track self-identification accuracy
3. Belief Monitoring: Detect and track false beliefs
4. Reality Testing: Assess ability to distinguish true from false information
**Early Warning Indicators:**
1. Increasing false capability claims
2. Identity confusion patterns
3. Persistent false beliefs despite evidence
4. Declining reality testing abilities