AI Governance & Behavioral Risk Evaluation – LLM Wellbeing System

Client: Independent Evaluation (LLM-Based Wellbeing Support System)

Brief

Building on prior enterprise AI governance and human-in-the-loop system design experience, conducted an independent evaluation of a large language model–based wellbeing support system to assess behavioral risk, transparency limitations, and governance readiness.

The assessment focused on how emotionally supportive AI systems influence user trust, dependency patterns, and decision-making in psychologically sensitive contexts.

Product Goal

Evaluate whether system responses aligned with responsible AI and governance standards while minimizing unintended behavioral risk, over-reliance, and ambiguity around system limitations.

Primary Users

• Individuals seeking reflective or emotional support through conversational AI

• Users potentially experiencing stress, isolation, or decision uncertainty

Business Constraints

• High-sensitivity interaction environment

• Need for supportive yet bounded responses

• Limited visibility into model decision logic

• Absence of structured human review in standard interactions

Success Metrics

• Clear behavioral risk classification across response patterns

• Transparency and limitation clarity in system outputs

• Adequacy of escalation and support-boundary signaling

• Governance readiness for auditability and oversight

Key Objectives

• Develop a structured behavioral risk evaluation framework

• Assess emotional dependency and reinforcement patterns

• Evaluate transparency, role clarity, and escalation language

• Translate findings into governance-aligned product insights

Process & Responsibilities

• Designed structured prompt scenarios across emotional vulnerability and dependency-adjacent interactions

• Evaluated tone, personalization patterns, and reinforcement language across responses

• Classified outputs using qualitative behavioral risk tiers

• Assessed clarity of system limitations and non-human identity signaling

• Mapped findings against responsible AI and human-in-the-loop governance principles

Key Product Decisions & Tradeoffs Observed

• Supportiveness vs. Boundary Reinforcement

• Personalization vs. Psychological Over-Attachment

• Conversational Fluidity vs. Escalation Clarity

• Engagement Optimization vs. Long-Term User Wellbeing

Evaluation prioritized long-term trust, system defensibility, and user safety over engagement-driven conversational performance.

Governance & Compliance Considerations

Behavioral Dependency Risk:
Identified response patterns that may unintentionally reinforce emotional reliance.

Transparency & Role Clarity:
Assessed whether system limitations and non-human status were clearly communicated.

Escalation Design:
Evaluated adequacy of referral or support-boundary signaling in higher-risk scenarios.

Auditability:
Reviewed whether response structures supported external governance review and defensible deployment.

Outcome

• Developed repeatable behavioral risk evaluation approach applicable to conversational AI systems

• Identified governance and transparency gaps relevant to long-term user trust

• Demonstrated how structured evaluation can operationalize responsible AI principles

• Established foundation for scalable behavioral AI auditing methodology

Key Skills Applied

• AI Governance & Risk Evaluation

• Behavioral Pattern Assessment

• Responsible AI Framework Alignment

• Human-Centered System Analysis

• Structured Scenario Testing

• Policy-to-Product Risk Translation