Health Information Technology Commons^™

Architectural Design Of A Blockchain-Enabled, Federated Learning Platform For Algorithmic Fairness In Predictive Health Care: Design Science Study, Xueping Liang, Juan Zhao, Yan Chen, Eranga Bandara, Sachin Shetty

VMASC Publications

Background: Developing effective and generalizable predictive models is critical for disease prediction and clinical decision-making, often requiring diverse samples to mitigate population bias and address algorithmic fairness. However, a major challenge is to retrieve learning models across multiple institutions without bringing in local biases and inequity, while preserving individual patients' privacy at each site.

Objective: This study aims to understand the issues of bias and fairness in the machine learning process used in the predictive health care domain. We proposed a software architecture that integrates federated learning and blockchain to improve fairness, while maintaining acceptable prediction accuracy and minimizing overhead …

Public Goods From Private Data: An Effectiveness And Justification Dilemma For Digital Contact Tracing, Andrew Buzzell

The Journal of Sociotechnical Critique

Debate about the adoption of digital contact tracing (DCT) apps to control the spread of COVID-19 has focussed on risks to individual privacy. This emphasis reveals significant challenges to ethical deployment of DCT, but generates constraints which undermine justification to implement DCT. It would be a mistake to view this result solely as the successful operation of ethical foresight analysis, preventing deployment of potentially harmful technology. Privacy-centric analysis treats data as private property, frames the relationship between individuals and governments as adversarial, entrenches technology platforms as gatekeepers, and supports a conception of emergency public health authority as limited by individual …