Addressing Health Disparities and Biases in AI and Machine Learning Tools
Health disparities are defined by NIH as differences in the incidence, prevalence, mortality, and burden of diseases or health conditions between specific population groups in the US. These disparities are often associated with demographic, social or economic factors such as income, race, gender, and geography (e.g. rural vs. urban), among others. Iowa is not exempted from these disparities. Several recent studies have reported that AI systems can discriminate and create unequal outcomes in different population groups. Because AI/ML models learn from historically collected data, human and structural biases present in the data can be perpetuated and even exacerbated as the models are applied to clinical care. The goal of this initiative is to identify and quantify current biases in AI/ML healthcare models and to develop interventions to correct and reduce these biases.
Jumpstarting a Quantum Simulation Program at The University of Iowa
Quantum simulation (QSim) can enable efficient discovery of drugs, biocompatible fertilizers, and high temperature superconductors, all key priorities as identified by the United Nations. Such simulations are not possible even with the largest supercomputers due to the limits of conventional computation, but quantum computers would be capable of solving these problems. This proposal aims to solve the convergent problem of constructing a practical QSim leveraging unique expertise at UI by creating a team of physicists, chemists, engineers, and mathematicians to concurrently tackle the fundamental as well as technical challenges. We aim to implement a practical and scalable QSim by i) developing semiconductor qubits operating at higher temperatures and lower costs than associated with competing technology and ii) building on UI strengths in nanofabrication (Iowa CREATES) and quantum algorithms. The long-term goal (<5 years) of placing UI on the map as a leader in quantum science research and development.