Interpretable Safety Routing for Foundation Models in Commercial Recommendation Pipelines Using Path-Level Causal Intervention
Keywords:
foundation models, recommendation systems, safety routing, causal intervention, interpretability, path-level analysis, content governance, microservice architecture, trustworthiness, regulatory complianceAbstract
The integration of large foundation models into commercial recommendation pipelines introduces novel safety challenges that extend beyond traditional content moderation. These models, trained on vast and heterogeneous corpora, can inadvertently generate harmful, biased, or policy-violating outputs when deployed in high-stakes recommendation contexts. Existing safety mechanisms, such as output filtering and prompt engineering, provide limited visibility into the causal pathways through which unsafe content emerges. This paper proposes an interpretable safety routing framework that leverages path-level causal intervention to identify and redirect unsafe information flows at intermediate stages of the recommendation pipeline. We conceptualize the pipeline as a directed graph of processing stages, where each edge represents a transmission of learned representations. By applying targeted interventions on selected paths and observing downstream effects, the framework enables system operators to diagnose the root causes of unsafe outputs and reroute representations through safer pathways without retraining the entire model. We examine the architectural trade-offs between intervention granularity, computational overhead, and interpretability, and discuss deployment considerations in multi-tenant, microservice-based infrastructures. The implications for governance, fairness, and policy compliance are analyzed through the lens of emerging regulatory frameworks and industry standards. This work contributes a system-level perspective on safety for foundation model-based recommender systems, emphasizing the importance of causal reasoning and interpretability in building trustworthy commercial AI pipelines.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
