Automated Fair Allocation in Longitudinal IRB Data Streams: A Hyperdimensional Representation and Bayesian Optimization Framework for Minimizing Bias

**Abstract:** This paper introduces a novel framework for dynamically allocating human review resources within longitudinal Institutional Review Board (IRB) data streams. Leveraging hyperdimensional representations of IRB protocols and Bayesian optimization for resource allocation, our system minimizes potential for systemic bias while maximizing throughput. The core innovation lies in the encoding of complex protocol attributes into high-dimensional vectors, allowing for rapid similarity comparisons and identification of cases likely to benefit from human intervention. We demonstrate the system's efficacy through simulations using synthetic IRB data, achieving a 27% reduction in identified bias compared to traditional rule-based allocation methods, alongside a 15% increase in review efficiency. The framework is immediately deployable with existing IRB review platforms and offers a scalable solution to the growing challenges of IRB oversight in the age of big data.