About
efrén studies the way algorithms reproduce bias and discrimination. Automated procedures are often designed to mimic the historical data humans have generated. Therefore, unintendedly, they have learned to discriminate based on class, race, gender, and other vulnerable groups. Such a phenomenon has serious consequences, as it may lead to furthering economic inequality, depriving the poor of resources, over-incarceration of people of color, etc. efrén’s goal is to understand the dynamics of the system the algorithm belongs to and assess which structural interventions are the best actions to both avoid discrimination and accomplish the desired goal for the population of interest.
efrén is also a Data Science Fellow with the Michigan Institute for Data Science (MIDAS)
