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Found 8 papers, 4 papers with code
Generalized People Diversity: Learning a Human Perception-Aligned Diversity Representation for People Images
First, a text-guided approach is used to extract a person-diversity representation from a pre-trained image-text model.
Consensus and Subjectivity of Skin Tone Annotation for ML Fairness
Our study shows that annotators can reliably annotate skin tone in a way that aligns with an expert in the MST scale, even under challenging environmental conditions.
A Step Toward More Inclusive People Annotations for Fairness
In this paper, we present a new set of annotations on a subset of the Open Images dataset called the MIAP (More Inclusive Annotations for People) subset, containing bounding boxes and attributes for all of the people visible in those images.
Measuring Non-Expert Comprehension of Machine Learning Fairness Metrics
Bias in machine learning has manifested injustice in several areas, such as medicine, hiring, and criminal justice.
Group Fairness in Bandit Arm Selection
We propose a novel formulation of group fairness with biased feedback in the contextual multi-armed bandit (CMAB) setting.
Transfer of Machine Learning Fairness across Domains
A model trained for one setting may be picked up and used in many others, particularly as is common with pre-training and cloud APIs.
Making the Cut: A Bandit-based Approach to Tiered Interviewing
Given a huge set of applicants, how should a firm allocate sequential resume screenings, phone interviews, and in-person site visits?
The Diverse Cohort Selection Problem
We apply our general algorithm to a real-world problem with combinatorial structure: incorporating diversity into university admissions.
