32 citations · 112 across the 6 of their papers we have counts for
7 papers
Consensus and Subjectivity of Skin Tone Annotation for ML Fairness
Candice Schumann, Gbolahan O. Olanubi, Auriel Wright +3
Understanding different human attributes and how they affect model behavior may become a standard need for all model creation and usage, from traditional computer vision tasks to t…
A Step Toward More Inclusive People Annotations for Fairness
Candice Schumann, Susanna Ricco, Utsav Prabhu +2
The Open Images Dataset contains approximately 9 million images and is a widely accepted dataset for computer vision research. As is common practice for large datasets, the annotat…
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…
Group Fairness in Bandit Arm Selection
Candice Schumann, Zhi Lang, Nicholas Mattei +1
We propose a novel formulation of group fairness with biased feedback in the contextual multi-armed bandit (CMAB) setting. In the CMAB setting, a sequential decision maker must, at…
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…
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…