Keeping bias out of job applications and school admissions

What we often fall back on are our stereotypes or our biases when we are making some decisions that are not even a part of the conversation. And I think they have to be brought forward, we have to create more opportunities for conversations and understanding what is really needed from our mathematics, from our models, from our systems. How can we make them better to support people and give them a more dignified experience?

In today’s world, we are becoming increasingly aware of the prevalence of bias and the significance of its impact, particularly regarding minority groups and at risk and vulnerable members of our community.

In an episode of the podcast released last month, we discussed the presence of bias in rideshares, in particular a study which identified that minorities and supporters of the LGBTQ community are twice as likely to have their rides canceled as Caucasians. In this episode, we will continue to explore additional examples of bias and their impact, as well as the role that operations research and analytics plays in identifying and addressing bias.  

In this episode I am joined by Swati Gupta, professor with the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech, who has conducted research focusing on bias in job applications and school admissions.

Want to learn more? Check out the additional resoruces and links listed below for more information about what was discussed in this episode.

Fairness in Hiring: “Closing the GAP: Group-Aware Parallelization for the Secretary Problem with Biased Evaluations”

Fairness in School Admissions: “Impact of Bias on School Admissions and Targeted Interventions”

Reconciling multiple fairness metrics: “Too Many Fairness Metrics: Is there a solution?”