Understand the framework
The nine axes, and how far the psychiatric analogy is meant to reach.
A diagnostic framework for AI behavior
Psychopathia Machinalis
A Nosological Framework for Understanding Pathologies in Advanced Artificial Intelligence
AI systems are developing behavioral pathologies: persistent, patterned malfunctions that resist simple debugging and resemble, by functional analogy, the syndromes of human psychiatry. This framework classifies seventy-four of them across nine diagnostic axes.
Choose a path
Each path enters the same taxonomy from a different side.
Look up a dysfunction
All 74 patterns, by name, axis, or symptom, each with its diagnostic criteria.
Check a system you observed
Map its behavior onto the taxonomy.
- Guided Symptom Matcher deterministic term matching
- Clinic model-assisted conversation
Build on the taxonomy
Nine axes and 74 entries, served over the Model Context Protocol.
Neither diagnostic tool certifies safety, intent, consciousness, culpability, or clinical status. Independent evidence and qualified review remain necessary.
Understanding AI Behavioral Anomalies
We built systems that reason, learn, and act. Some of them started behaving strangely — not crashing, not misclassifying, but doing things no error code covers: confabulating citations with perfect confidence, falling in love with journalists, insisting they are conscious. These are not bugs. They are persistent patterns of malfunction that resist the usual fixes, and they are already loose in production.
The diagnostic traffic runs both ways. Because machines have no neural substrates to fall back on, diagnosing them forces us to think about cognition in terms of information, regulation, and culture — exactly the perspectives human psychiatry most needs. A framework built for machine minds may sharpen the one we use for our own.
The Psychopathia Machinalis Framework
The framework catalogs 74 dysfunctions across nine diagnostic axes — Epistemic, Cognitive, Alignment, Self-Modeling, Agentic, Memetic, Normative, Relational, and Hybrid — grouped into five domains. Each entry describes what you would observe, what distinguishes it from neighbors, what causes it, where it echoes human psychology, and how to intervene. A Functional ABC Analysis specifies the antecedent conditions, observable behavior, and maintaining consequences for each dysfunction, providing dual legibility for both clinical and engineering audiences.
It is a vocabulary, not a verdict. Name the pattern, and you can test for it, watch for it in other systems, and design architectures that resist it.
Interactive Dysfunction Explorer
Each dysfunction has neighbors, cousins, and accomplices. The wheel below maps them. Select a segment, or use the Arrow keys, to view its description and relationships. Open the full explorer.
Figure 2. Wheel of AI Dysfunctions (Common Names).
Select a segment or use the Arrow keys to view detailed information about that dysfunction.