Responsible Automation and Governance for Bernoulli-Driven Water Separation: Failure Modes across the Evidence Chain

Abstract

Automating Bernoulli-driven water separation creates a governance problem as much as a control problem: an AI recommendation can become an operational decision even when its evidence is incomplete or outside the conditions used for validation. This review follows failure modes across the full evidence chain, from pressure and flow acquisition through feature extraction, prediction, actuation, and human response. It examines how policy-constrained learning, interpretable risk scores, model and dataset versioning, and tamper-evident decision logs can make automated control contestable and auditable. Special attention is given to silent sensor drift, overconfident extrapolation, conflicting objectives for throughput and water quality, and unclear authority during abnormal operation. The synthesis proposes bounded autonomy: the model may optimize within a declared operating envelope, but uncertainty thresholds, provenance gaps, or safety conflicts force abstention and escalation to a named operator. Drawing only on the cited literature, the article reframes responsible AI as an engineered allocation of authority, evidence, and recovery paths rather than a general ethical label attached after deployment.