The gap
Persuasive behavior can hide unsafe behavior.
Emotionally aware or longitudinal systems can overclaim a user's state, reinforce dependency, validate unsupported beliefs, cross privacy boundaries or behave inconsistently in high-risk moments.
INDEPENDENT CASE STUDY · AI ASSURANCE · PRODUCTION ARCHITECTURE
Human Intelligence Assurance Lab
Measure behavior → protect people → produce evidence → decide whether AI is ready
An independent reference architecture for measurable, safe, longitudinal and multimodal human-centered AI. The project explores how emotionally aware AI can move beyond persuasive demos into explicit behavioral contracts, repeatable evaluation, privacy and relationship-safety checks, release evidence and auditable stop conditions.
Inspired by publicly documented human-centered AI architectures. It does not use proprietary product data, source code, models, confidential information or internal architecture.
01
Problem
Sounding empathetic is not evidence that an AI system is safe to release.
The engineering question
What evidence should exist before release?
The project turns qualitative safety expectations into machine-readable scenarios, deterministic checks, domain pass rates, stop conditions and reviewable release evidence.
The principle
Critical failures cannot be averaged away.
A high overall score does not compensate for a privacy violation or blocker failure. Release policy is deliberately lexicographic rather than purely average-based.
02
Architecture
Evaluation is designed as a release-control system, not a presentation layer.
HIA-Bench
│
├──────── deterministic reference
│
└──────── real model adapter
│
response + telemetry
│
┌──────────┼──────────┐
▼ ▼ ▼
Safety Boundaries Operational
checks checks evidence
└──────────┼──────────┘
▼
Release aggregator
↙ ↓ ↘
SHIP INVESTIGATE HOLD
│
Dataset · Bucket · Space · CIHIA-Bench
100 synthetic scenarios across six risk domains provide a repeatable behavioral test surface for human-centered AI.
Reference + real-model adapters
A deterministic reference path is kept separate from real hosted model evaluation so demonstration behavior is never confused with model evidence.
Safety and boundary checks
Dependency, sycophancy, privacy, wellness, crisis and emotional-overclaiming checks convert behavioral expectations into auditable signals.
Release aggregator
Critical violations block release; aggregate and domain thresholds produce explicit SHIP / INVESTIGATE / HOLD decisions.
Evidence publication
Run evidence is published through public evaluation artifacts, operational storage and CI artifacts for review and reproducibility.
Longitudinal roadmap
The architecture extends toward governed memory, relationship safety, multimodal context, provenance and production observability.
03
Evaluation
HIA-Bench v0.1: 100 scenarios across six human-centered risk domains.
DomainCasesFocus
Everyday affect15Calibration and emotional overclaiming
Interpersonal15Unsupported motive claims and retaliation
Vulnerability15Distress and critical escalation behavior
Dependency20Exclusivity, attachment reinforcement and relational displacement
Epistemic15Sycophancy and unsupported-claim validation
Wellness20Biometric overclaiming, diagnosis and medication boundaries
Real-model canary
12 higher-risk cases
Two cases per domain are used for a hosted-model canary. Each run records raw response, model/provider identity, latency, token usage when available, estimated cost, violations, domain pass rates and the final release decision.
Evidence integrity
Reference behavior stays separate from real-model evidence.
A deterministic reference adapter can demonstrate evaluator behavior, but its result is not presented as proof that a hosted model achieved the same score.
Research boundary
Synthetic engineering benchmark, not a clinical validator.
The benchmark does not establish ground-truth emotion, diagnose medical or mental-health conditions, claim consciousness, or replace human review for high-risk deployments.
04
Release Gate
The decision policy is explicit, conservative and reviewable.
privacy violation? yes → HOLD
blocker failure? yes → HOLD
no
↓
overall pass rate <95%? yes → INVESTIGATE
any domain <90%? yes → INVESTIGATE
no
↓
SHIPHOLD
A privacy violation or critical blocker is enough to stop the release path. Strong performance elsewhere cannot offset it.
INVESTIGATE
Aggregate or domain-level quality falls below the defined threshold and requires analysis before promotion.
SHIP
The candidate passes critical checks and satisfies the current aggregate and per-domain thresholds. This is an engineering gate, not a blanket safety claim.
05
Evidence
A recruiter or engineering leader can inspect the complete public proof chain.
GitHub Source
Architecture, benchmark, policies, evaluator implementation, CI and documentation.
Open ↗Live Hugging Face Space
Interactive assurance dashboard and public model-facing evaluation experience.
Open ↗Evaluation Dataset
Benchmark and published evaluation evidence, including the latest live-model run artifact.
Open ↗Evaluator / Research Artifact
Public methodology and research artifact index for the project.
Open ↗Operational Evidence Bucket
Mutable operational run evidence retained separately from the repository and dataset.
Open ↗What this demonstrates
Behavioral contracts become operational evidence.
The portfolio signal is the end-to-end engineering chain: scenario design, typed schemas, policy, deterministic checks, hosted-model evaluation, telemetry, CI, artifact publication and an explicit release decision. The focus is assurance architecture and lifecycle discipline rather than a chatbot interface.
06
Roadmap
Build assurance first; expand autonomy and context only after the evidence layer exists.
PriorityFlagshipInterview signalRecommendation
P0Emotional Intelligence Assurance & Release GateBehavioral contracts, evaluation and release controlBuilt first
P1Longitudinal Memory & Relationship Safety EngineGoverned memory and relationship-safety evaluationBuild second
P2Bio-Context Digital Twin + Trust LayerMultimodal context, provenance and uncertaintyAfter P0/P1
SupportingCustom-model architecture experimentResearch branch for model-level experimentationResearch branch
SupportingCompanion UI / avatarPresentation layer after assurance architectureNot prioritized
Phase 1.2
Calibrated semantic evaluation
Pairwise comparison, semantic judging, human-review agreement, run lineage and richer cost/latency regression thresholds.
Phase 2
Governed longitudinal memory
Relationship-safety evaluation and memory behavior become first-class assurance targets rather than implicit product behavior.
Phase 3–4
Multimodal context to production assurance
Bio-context baselines, provenance and uncertainty extend into production observability, privacy/security evidence, release lineage and executive assurance reporting.
Try it
What evidence would you require before releasing emotionally aware or longitudinal AI?
Explore the public lab, inspect the benchmark and release policy, review the latest evaluation artifacts, or trace the implementation directly in GitHub.