Responsible Automation and Governance for Lithium-Sulfur Separator Engineering: From Laboratory Evidence to Operational Control

Abstract

As lithium-sulfur separator research moves toward AI-assisted manufacturing and battery management, optimization decisions may affect safety, durability, and material acceptance before their evidential basis is fully visible. This review defines responsible automation as a technical control structure for translating laboratory evidence into bounded operational authority. Learned models may rank material batches, detect degradation, or recommend cycling adjustments, but policy constraints protect non-negotiable safety limits and require calibrated uncertainty, provenance, and human override. The synthesis maps common governance failures: untraceable training data, proxy objectives that reward short-term capacity, silent model updates, automation bias, and missing appeal paths for rejected batches. It proposes role-based approval, versioned models and datasets, recorded rationales, staged release, and independent monitoring of both physical and algorithmic drift. No automated production trial is claimed. The article's contribution is a source-grounded AI governance blueprint in which every recommendation can be traced from separator mechanism and measurement through validation to the accountable person who authorizes action.