Non-model gains via inference, systems, and assets can drive AI capabilities independently of base models, requiring governance beyond model-level evaluation and mitigation.
Beyond privacy trade-offs with structured transparency,
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
A semi-air-gapped RDS framework using PySyft enables inter-institutional student retention prediction with Macro F1 scores of 0.690-0.695 while preserving privacy via synthetic data and strict data isolation.
citing papers explorer
-
Comprehensive AI governance requires addressing non-model gains
Non-model gains via inference, systems, and assets can drive AI capabilities independently of base models, requiring governance beyond model-level evaluation and mitigation.
-
A Privacy-Preserving Framework Using Remote Data Science for Inter-Institutional Student Retention Prediction
A semi-air-gapped RDS framework using PySyft enables inter-institutional student retention prediction with Macro F1 scores of 0.690-0.695 while preserving privacy via synthetic data and strict data isolation.