Hybrid Expert-Augmented Active Learning for Enhanced Electronic Records Management in Uganda’s Wildlife Sector

Hybrid Expert-Augmented Active Learning for Enhanced Electronic Records Management

Abstract

We propose a hybrid expert-augmented active learning framework to reformulate
Uganda’s wildlife electronic records management system, addressing the critical
challenges of data quality and decision-making efficiency in conservation efforts.
The proposed method integrates a Bayesian neural network with human-in-the-
loop annotation, dynamically prioritising uncertain records for expert validation
while autonomously processing high-confidence data. The system consists of
three core modules: an uncertainty-aware data ingestion layer that quantifies
prediction reliability, a mobile-optimised expert interface for real-time
annotation, and an adaptive training loop that incrementally refines the model
using newly validated records. Moreover, the architecture substitutes
conventional data pipelines by routing ambiguous inputs to human experts and
archiving only machine-confident entries, thereby reducing noise in the central
database. The implementation employs a Monte Carlo dropout transformer for
robust uncertainty estimation and federated learning to aggregate distributed
expert inputs without centralised data pooling. Unlike static systems, our
approach establishes a closed-loop feedback mechanism between data quality
and model performance, enabling continuous improvement in predictive
accuracy and operational decision-making. The novelty lies in its context-aware
annotation workflow and dynamic prioritisation of expert effort, which are
tailored to the sparse and heterogeneous nature of wildlife data in resource-
constrained environments. Field deployments demonstrate significant
improvements in species identification accuracy and threat assessment reliability,
highlighting the framework’s potential to transform electronic records
management in conservation sectors globally.