C G Filippi, J M Stein, Z Wang, S Bakas, Y Liu, P D Chang, Y Lui, C Hess, D P Barboriak, A E Flanders, M Wintermark, G Zaharchuk, O Wu
In this review, concepts of algorithmic bias and fairness are defined qualitatively and mathematically. Illustrative examples are given of what can go wrong when unintended bias or unfairness in algorithmic development occurs. The importance of explainability, accountability, and transparency with respect to artificial intelligence algorithm development and clinical deployment is discussed. These are grounded in the concept of "primum no nocere" (first, do no harm). Steps to mitigate unfairness and bias in task definition, data collection, model definition, training, testing, deployment, and feedback are provided...
August 31, 2023: AJNR. American Journal of Neuroradiology