arXiv:2607. 09259v1 Announce Type: cross Abstract: Telecom fraud-control studies often stop at detector-level classification, but deployment use requires request-level policy resolution, lifecycle traceability, and auditability.
By Saviz Changizi, Nasibeh Mohammadzadeh, Mohammad Shojafar, Rahim Tafazolli
arXiv:2608. 08577v1 Announce Type: new Abstract: Fraud operations must allocate events among automatic approval, analyst review, and automatic blocking even though the labels needed to evaluate these actions are selective and delayed.
By Jie Deng (Tongji University, Shanghai, China)
The paper introduces Governance-Aware Federated Learning (GAFL), a framework that enhances traditional federated learning with independent governance verification, fairness and explainability assessment, anomaly detection, policy enforcement, and tamper‑evident audit logging. In a cross‑silo prototype with 10 participants, GAFL successfully identified all falsified‑evidence participants and intervened on affected participants across various threat scenarios, achieving macro‑F1 scores between 0.7520 and 0.7733. The added governance processing time per round was 173.67 ± 26.94 ms, compared to 3.82 ± 0.79 ms for standard FedAvg, highlighting a trade‑off between fairness policy enforcement and utility.
By Kiranmayee Janardhan, Karthick S, Madhusmita Panda, Bharathi Vokkaliga Ganesh, Syed Ahmed
DeFiRank is a federated learning-based pairwise ranking framework designed to improve governance in decentralized financial networks. It allows multiple institutions to collaboratively train a ranking model without sharing private transaction data, capturing network-wide behavioural patterns such as diversity of counterparties, multi-application usage, and transaction frequency. Evaluated on a real-world blockchain dataset, DeFiRank achieves ranking performance close to a centralized baseline while preserving data privacy and maintaining ranking stability, enabling reliable provider promotion and demotion decisions.
By Zahra Batool, Michael Duchesne, Kaiwen Zhang, Ulrich Aivodji
The paper "Governing at Machine Speed: An Adaptive Intelligence Architecture for Real-Time AI Policy Enforcement" highlights a gap in enterprise AI governance, where 78% of organizations lack auditable evidence of policy enforcement. It introduces AGIL, a five-layer adaptive governance architecture that uses machine learning for real-time detection, risk classification, sub-100ms policy enforcement, continuous attestation, and policy evolution. The authors argue that the failure is organizational and architectural, not technical, and call for future empirical validation of AGIL.
By Sandeep Bokkasam, B. Durgalakshmi
The paper introduces a protocol for auditing and composing reinforcement‑learning policies using discrete behavioral rules, defining auditability through six testable predicates such as trace integrity and rule coverage. Experiments show that overlapping rule sets do not guarantee behavioral agreement, and that rule‑based fusion often fails to outperform value‑based composition, highlighting limitations in current description layers. The authors provide an evidence‑bounded audit framework and outline future directions for more robust skill composition.
By Liu Hung Ming