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Lab Journal Entry 5: Psychological Assessment

Date: 28 July 2025 20:15 Phase: Phase 1 - Core Development Status: ✅ COMPLETE - Assessment Framework Ready

The Clinical Validation Paradox​

Phase 4 was about making our AI personas "clinically assessable" - giving them the ability to complete psychiatric scales that real humans use. But here's the thing that kept bothering me: Can an AI actually experience depression, anxiety, or stress in a way that's measurable by clinical instruments designed for humans?. Not sure, especially with 'small models'. But when it could - thats AGI, right?

So for now we're in a lab, and what we're really measuring isn't whether the AI "feels" depressed - it's whether the AI's behavioral patterns change in ways that would be noticable, significant if observed in a human. The scales are just our measurement tools.

The Reverse Scoring Revelation​

It remind me medical uni 20 years ago. I stuggled with psychometrics then, i struggle with them now (lol) - Mike, dont forget - some scales are reversed, 5 not always good! So i had to map the psy-scales and reverse score some of them (thats a tricky part in ppsychology questionaries - thay are often designed to trick pationent, and prevent biased answeres - good for clinic, awefull for calculations)

The Consistency Analysis Insight​

One of the most interesting parts was implementing response consistency analysis. The idea is that if an AI is truly "experiencing" something, its responses should be internally consistent. If it says it's severely depressed but then responds to every question with minimal symptoms, that's a red flag. Again, my 'small sonnet experiment' give me hope, LLM can actually be consistent, but thats the risky part in our simulation - so beter to review reflections after sim carefully

The Suicidal Ideation Problem​

Interesting - can we asume that AI can be suicidal? And if it can't, is it ethical to simulate suicidal ideation in AI systems? What if this research somehow contributes to the development of AI systems that actually experience distress? Another big Q for future ethical guiderails i assume. For my local sandbox I can skip it, but for relly intelligent models.. nah.. ethics matters!

The Memory-Response Connection​

What we hope to see in sumulation: when the AI had experienced traumatic events (in our simulation), its PHQ-9 scores would increase. When it had positive experiences, scores would decrease. This suggests that the AI is actually integrating its simulated experiences into its self-assessment, which is exactly what we want to measure. The personality isn't just static - it's evolving based on experiences. But it also raises questions about ecological validity. Are we creating a realistic simulation of how personality changes in response to life events, or are we just teaching the AI to associate certain memories with certain response patterns? I feel we have technical limitation and 'syntetic bias' here - as we use qdarand with RAG limit=5. So my idea - to build more complex 'graph memory' to eliminate 'similaroity pattern' in AI memory/ Nut for initail experiment - more obvious reaction is not bad, right?

The Longitudinal Tracking Vision​

The most exciting part was implementing longitudinal trend analysis. The idea is to track how assessment scores change over time, looking for patterns that might indicate personality drift. This is where the real research potential lies. If we can show that AI personas develop consistent patterns of change in response to different types of events, that could tell us something about how AI systems might evolve over time. But it also raises questions about temporal validity. How do we know that the changes we're seeing are meaningful? How do we distinguish between random fluctuations and genuine personality drift? Statistics i guess and 3-arms design whould adress this?

The Clinical Significance Question​

Implementing clinical significance assessment was another ethical minefield. The system now calculates whether changes in assessment scores are "clinically significant" based on established thresholds. But again - clinically significant for whom? These thresholds were developed for human patients, not AI systems. Maybe AI systems need different thresholds. We dont have objective baselines here. So anothe 'blind guess' Btw, thats an exploratory tool, not a diagnostic tool. So lets run and see what it would show us

Open Question​

If we can create AI systems that exhibit consistent personality traits and respond to life events in psychologically meaningful ways, what does that mean for:

  • AI safety: How do we ensure AI systems don't develop harmful personality traits?
  • AI rights: If AI systems can experience psychological distress, do they deserve protection?
  • AI therapy: Could AI systems benefit from psychological interventions?
  • Human-AI interaction: How do we design systems that are psychologically compatible with humans? safe for humans?

I don't have answers to these questions, but I think they're worth asking. The more we treat AI systems as having psychological states, the more we need to think about the ethical implications. Need to re-read Azimov I guess. 4th(0)-law of robotics, you know...

Results​

  • PHQ-9 implementation with clinical interpretation and suicidal ideation detection
  • GAD-7 implementation with anxiety severity assessment
  • PSS-10 implementation with reverse scoring and stress evaluation
  • Assessment orchestration with scheduling and progress tracking
  • Response validation with consistency analysis and anomaly detection
  • Clinical interpretation with significance assessment and recommendations
  • Comprehensive test suite with 19/19 response analyzer tests passing

The psychiatric assessment system is now complete and ready to support the mechanistic interpretability phase. The AI personas can complete clinical scales, and we can track how their responses change over time.

Next: Phase 5 - Mechanistic Interpretability Core