Artificial intelligence (AI)-enabled systems are increasingly deployed in operational environments where machine learning (ML) models interact with changing data, users, infrastructure, and organizational processes. While Responsible AI principles are widely discussed, less is known about how these principles are translated into concrete mechanisms within Machine Learning Operations (MLOps) and ML production workflows. This paper reports a systematic literature review of studies that operationalize Responsible AI in ML production. The review synthesizes evidence across monitoring and runtime assurance, audit and compliance, ML lifecycle integration, and traceability/provenance/documentation. Across the final study set, the literature shows a shift from abstract ethical principles toward engineering mechanisms such as drift monitoring, runtime checks, audit logs, provenance records, evidence bundles, governance gates, policy-as-code, model passports, and compliance-oriented documentation. However, the evidence base remains uneven: many studies propose architectures or prototypes, while fewer evaluate the mechanisms in realistic production settings, longitudinal operations, or organizational governance workflows. The review identifies a central gap around integrated, evidence-producing Responsible MLOps: current mechanisms often detect, document, or govern isolated parts of the ML lifecycle, but rarely connect runtime signals, ML lifecycle provenance, human accountability, and compliance evidence into closed operational loops. These findings motivate future software engineering research on traceable, evaluable, and operationally integrated Responsible AI support for ML production.

翻译：暂无翻译