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Sponsored Artificial Intelligence: A Modern Approach, Global Edition Artificial Intelligence: A Modern Approach, Global Edition

Author

Eklachur Rahman Bhuiyan

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Preprint Aug 2026

ZK-SR117: A Chunked Zero-Knowledge Attestation Design for Aggregated Fair-Lending Metrics, with a Control Mapping toward Full SR 11-7 Coverage

Deploying ML models in regulated decision-making (credit underwriting, fraud detection, loan approval) requires demonstrating fairness and robustness to auditors without exposing model weights or customer data. We address this attestation problem for U.S. bank supervision under SR 11-7 and OCC 2011-12 guidance. We present a chunked zero-knowledge circuit design that attests an aggregated fairness statistic - the demographic-parity gap - on committed, nonce-sampled batches of real 2022 HMDA mortgage data, and demonstrate it end-to-end: 32,768 rows, 32 independently verified zkSNARK proofs, aggregated attested gap within 0.0029 of the true held-out value, per-chunk proving under 4 seconds. We also demonstrate extensibility by attesting a second control on the identical architecture - expected calibration error at 10 bins - with all 32 chunks verified, per-chunk proving at about 14.7 seconds, and attested ECE within 0.00037 of the plaintext value on the same committed rows. We compare this design against two alternatives - a flat summation circuit, which overflows past a few thousand rows, and a tree-reduction circuit, numerically exact but intractable to compile - and find the chunked design is the only one that reached this scale. We discovered and root-caused a genuine data-quality failure (a sentinel-code outlier distorting circuit proving and the fairness statistic itself) and resolved it with a published preprocessing specification. We also propose a fuller mapping from SR 11-7 and OCC 2011-12 control language to zero-knowledge statements (nine control elements spanning soundness, calibration, robustness, and drift), a nonce-based sampling protocol resisting bank-side cherry-picking, and a threat model for deployment, proposed as design work, not implemented results. Two controls, one model class, one task are demonstrated end-to-end; the rest is scoped and left as future work.

Mohammad Nasir Uddin, Rahnuma Tabassum Orpita, Eklachur Rahman Bhuiyan et al. · 0 citations
Open access Jul 2026

Machine Learning-Based Early Prediction of Hospital Readmission Risk Among Chronic Disease Patients Using Electronic Health Records: A Comparative Study of Ensemble Learning Models

Hospital readmission among patients with chronic diseases remains a major challenge for healthcare systems due to its association with poor patient outcomes and increased healthcare costs. This study proposes a machine learning-based framework for the early prediction of 30-day hospital readmission risk using the publicly available Diabetes 130-US Hospitals dataset from the UCI Machine Learning Repository. A comprehensive preprocessing pipeline, feature engineering, and feature selection techniques were employed to improve data quality and predictive performance. Eight supervised machine learning algorithms, including Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, LightGBM, CatBoost, Multilayer Perceptron, and XGBoost, were developed and comparatively evaluated. Model performance was assessed using accuracy, precision, recall, F1-score, specificity, and the area under the receiver operating characteristic curve (AUC-ROC). The experimental results demonstrated that ensemble learning models consistently outperformed conventional machine learning approaches. Among all evaluated models, XGBoost achieved the best performance, attaining 92.16% accuracy, 0.92 precision, 0.91 recall, 0.91 F1-score, 0.95 specificity, and an AUC-ROC of 0.972. These findings indicate that XGBoost effectively identifies patients at high risk of early hospital readmission and can serve as a reliable predictive tool for clinical decision support. The proposed framework has strong potential for integration with Electronic Health Record systems to facilitate early intervention, improve patient outcomes, reduce preventable readmissions, and support value-based healthcare delivery.

Md Yassir Mottalib, Eklachur Rahman Bhuiyan, Anwar Hossain et al. · 0 citations