Enterprise AI Analysis
CPEMH: Behavioral Assurance for AI in Mental Health Screening
CPEMH introduces an agentic framework for prompt-driven behavioral evaluation and assurance in foundation-model (FM) systems, specifically for mental health screening using transcript-based data. It operationalizes behavioral assurance by orchestrating modular agents, computing advanced metrics like bias and robustness alongside accuracy, and enforcing reproducibility. The framework aims to transform prompt engineering into a controlled, auditable pipeline, ensuring stable, explainable, and reproducible AI behavior in sensitive clinical contexts.
Key AI Impact Metrics
CPEMH delivers measurable improvements in AI reliability for sensitive applications.
0.687
Max F1-Score (In-Sample)
<0.03
F1-Shift (IS vs. OOS)
~80%
Behavioral Stability (% Reduced Variance)
Deep Analysis & Enterprise Applications
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Agentic Frameworks
Prompt Engineering
Behavioral Assurance
CPEMH’s multi-agent architecture uses an Orchestrator Agent to manage workflow, an Inference Agent for LLM execution, and an Evaluation Agent for performance metrics. This modular design supports interpretability, reproducibility, and behavioral assurance, crucial for sensitive domains like mental health. It aligns with recent advances in agentic AI that leverage multi-agent orchestration for complex task automation, but distinctively prioritizes behavioral reliability.
Key principles include autonomous orchestration of design-evaluation-selection loops, traceable evaluation with metrics aligned to behavioral properties (bias, robustness, consistency), and quantifying/minimizing context-driven variability.
The framework transforms prompt engineering from ad hoc experimentation into a controlled and auditable pipeline. It systematically generates prompt variants using predefined strategies (e.g., Direct Instruction, Role-Based, Chain-of-Thought) and applies style rules. This allows for scalable exploration of prompt space and ensures uniformity in task framing. Evaluation metrics guide the selection of optimal prompts, prioritizing both predictive performance and behavioral stability.
The study found that simpler prompts (Direct Instruction, Role-Based) often outperform complex reasoning chains (Chain-of-Thought, Adaptive CoT), as complex reasoning can amplify context sensitivity, leading to less stable behavior.
CPEMH operationalizes behavioral assurance by integrating metrics for bias, robustness, and consistency beyond traditional accuracy. Bias (Precision-Recall difference) measures the balance between precision and recall, crucial for minimizing false negatives in mental health screening. Robustness (standard deviation of F1-score across runs) quantifies stability against contextual perturbations.
High consistency across different prompt variants and data subsets indicates predictable behavior. This metric-driven approach ensures that selected prompts meet thresholds for fairness and stability, supporting regulatory compliance and user trust in clinical decision support systems.
DI-2
Top Performing Prompt Strategy (Direct Instruction)
Enterprise Process Flow
Sample Data & Prepare
→
Design & Generate Prompts
→
Execute LLM Inference
→
Compute Metrics & Analyze
→
Rank & Recommend Prompts
→
Out-of-Sample Validation
| Prompt ID | Approach | F1 | Accuracy | Precision / Recall |
|---|---|---|---|---|
| DI-2 | Direct Instruction (DI) | 0.687 | 0.652 | 0.54/0.91 |
| RP-3 | Role-Based Prompting (RP) | 0.666 | 0.643 | 0.56/0.84 |
| CBP-2 | Constraint-Based Prompting (CBP) | 0.664 | 0.639 | 0.51/0.93 |
| DI-1 | Direct Instruction (DI) | 0.662 | 0.640 | 0.52/0.91 |
| DI-3 | Direct Instruction (DI) | 0.660 | 0.637 | 0.50/0.92 |
Case Study: Mental-Health Screening with DAIC-WOZ
The framework was applied to the Distress Analysis Interview Corpus - Wizard-of-Oz (DAIC-WOZ) dataset, comprising 189 transcribed clinical interviews for depression presence. This dataset is a clinically validated resource, crucial for mental health research.
Data was partitioned into In-Sample (IS) for initial design and Out-of-Sample (OOS) for robust validation. Seven prompt-strategy families (e.g., DI, RP, CoT) with 28 total configurations were evaluated. Results showed that the recommended DI-2 prompt maintained stable performance (macro-F1 ≈ 0.57, accuracy ≈ 0.57) in OOS, confirming generalization and behavioral stability. This demonstrates CPEMH's capacity to stabilize and audit foundation-model behavior in conversational and clinically sensitive domains.
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