AIDF: AI Development Framework
SmartHaus Research • 2024
Abstract
AIDF provides a mathematically grounded framework for designing, validating, and operating enterprise AI systems. It unifies governance, ethics, memory, orchestration, monitoring, data preparation, output refinement, and task management into a provable lifecycle.
Framework Overview
Governance
Approval chains, policy rules, audit sinks, and risk tolerance, enforced as controls‑as‑code.
Ethics & Compliance
Bias detection, fairness metrics, explainability, privacy requirements, and audit schedules.
Memory
Session memory, vector search, RAG/CRAG, caching, and holographic memory configurations.
Orchestrator
Workflow strategy, error handling, resource allocation, and observability integration.
Monitoring
Backend, anomaly detection, dashboards, SLOs, and alerting with structured SLAs.
Data Preparation
Nulls/duplicates/outliers checks, quality thresholds, and feature engineering controls.
Output Refinement
PII removal, required fields, and output length constraints for post‑processing.
Task Manager
Scheduling, concurrency, worker counts, and queue sizing aligned to workload.
Regulatory Alignment
AIDF aligns with ISO 42001, NIST AI RMF, GDPR/CCPA, SOX/Basel (financial), and HIPAA/FDA (healthcare). Each control is mapped to evidence and runtime assertions for audit readiness.
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