detAI
detAI is a term used to describe a class of artificial intelligence systems designed to be deterministic, auditable, and reproducible. Unlike many modern ML models that rely on stochastic optimization and non-deterministic parallelism, detAI aims to produce identical outputs for the same inputs and configuration, enabling easier verification and governance in high-stakes settings.
Core principles include deterministic inference, data and model versioning, output provenance, and transparent decision logic. detAI
Architecture and approaches: deterministic cores may employ rule-based components, symbolic reasoning, or constrained machine learning models
Applications: regulatory compliance, financial risk models, healthcare decision support, safety-critical systems such as autonomous vehicles, and
History and reception: concerns about reproducibility and accountability in AI research have driven interest in detAI
Challenges: balancing determinism with scalability and performance; integrating deterministic data pipelines with modern hardware accelerators; ensuring
See also: deterministic algorithms, reproducibility in AI, explainable AI, AI governance, audit trail.