Why Is It So Hard To Build An Ethical ML Framework For Healthcare

"A disproportionate amount of power lies with research teams who, after determining the research questions." The improved methods of collecting high-quality data, coupled with advancements of machine learning models fueled a new wave of healthcare practices. From retinopathy to computer vision-based surgeries, algorithms have found their ways into critical life-saving domains. The potential is tremendous, but somehow the world is cynical about a total embrace. This is because of the many ways in which bias creeps up into data and eventually to diagnosis.