3 papers
cs.CY2020
Measuring Non-Expert Comprehension of Machine Learning Fairness Metrics
Debjani Saha, Candice Schumann, Duncan C. McElfresh +3
Bias in machine learning has manifested injustice in several areas, such as medicine, hiring, and criminal justice. In response, computer scientists have developed myriad definitio…
cs.LG2019
Transfer of Machine Learning Fairness across Domains
Candice Schumann, Xuezhi Wang, Alex Beutel +3
If our models are used in new or unexpected cases, do we know if they will make fair predictions? Previously, researchers developed ways to debias a model for a single problem doma…
cs.LG2019
Making the Cut: A Bandit-based Approach to Tiered Interviewing
Candice Schumann, Zhi Lang, Jeffrey S. Foster +1
Given a huge set of applicants, how should a firm allocate sequential resume screenings, phone interviews, and in-person site visits? In a tiered interview process, later stages (e…