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@article{189637,
        author = {Sridevi Marisetti and George Richards and Hanumat Sanyasi Nouduri},
        title = {Machine Learning as a Quantum Ethical Sentinel: Forecasting and Mitigating Existential Risks from Superintelligent AI in a Quantum Future},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {7},
        pages = {7185-7192},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=189637},
        abstract = {In order to foresee and reduce existential risks presented by superintelligent AI in a future improved by quantum technology, this study introduces the Quantum Ethical Sentinel, a real-time risk forecasting system. To guarantee signal integrity, the system starts with thorough exploratory data analysis and multi-stage noise removal, utilizing a constantly updated Quantum Ethical Sentinel dataset.  In order to achieve near-perfect performance (Accuracy, Precision, Recall and F1 Score all at 0.99), a Gradient Boosting Classifier is trained on the cleaned, high-fidelity data. This allows for robust discriminating of emergent risk patterns. A Streamlit-based interface is used to deploy the model, which takes in streaming inputs, calculates risk scores in real time and outputs a forecasted risk level with interpretability capabilities that support ethical oversight.  This work provides a useful sentinel mechanism for proactive governance of advanced AI systems by fusing high-performance machine learning, user-facing real-time prediction and quantum-aware ethical signal processing. It gives stakeholders actionable intelligence and early warning capabilities to help prevent catastrophic outcomes.},
        keywords = {AI, Ethics, Gradient Boosting, Machine Learning, Quantum Computing, Real-Time Prediction, Risk Forecasting and Streamlit.},
        month = {December},
        }