Authors: Pawar Pritesh Sandip
Abstract: This paper presents a design in which every decision produced by a cloud-hosted AI model is examined by a dedicated Responsible AI layer before anyone acts on it. Cloud platforms now run models that classify, predict and recommend in settings where errors are costly, yet an accuracy score alone cannot show whether a particular decision was fair, private, secure or adequately justified. The proposed framework adds an assessment stage downstream of the model. This stage tests each output against fairness, privacy, security and reliability criteria, attaches a feature-level explanation generated with SHAP or LIME, merges the findings into a single risk label, and passes high-risk or uncertain outputs to a human reviewer. Every final decision is written to an audit log, and monitoring follows model behaviour, input drift and responsible-AI risks over time. The paper lays out the architecture, a thirteen-step decision procedure, a compact prototype plan built on Python, Scikit-learn, SHAP, LIME, Fairlearn and a public cloud, and an evaluation plan that contrasts the framework with an unmodified baseline. Because the prototype has not yet been built or tested, no experimental results are reported. The contribution is the integrated design together with a measurable plan for validating it.
DOI: https://doi.org/10.5281/zenodo.23726342
