Candice Schumann

Publication Activity (10 Years)

Years Active: 2017-2024
Publications (10 Years): 17

Top Topics

Human Perception

Resource Allocation

Top Venues

Publications

Hansa Srinivasan, Candice Schumann, Aradhana Sinha, David Madras, Gbolahan Oluwafemi Olanubi, Alex Beutel, Susanna Ricco, Jilin Chen
Generalized People Diversity: Learning a Human Perception-Aligned Diversity Representation for People Images. CoRR (2024)
Hansa Srinivasan, Candice Schumann, Aradhana Sinha, David Madras, Gbolahan Oluwafemi Olanubi, Alex Beutel, Susanna Ricco, Jilin Chen
Generalized People Diversity: Learning a Human Perception-Aligned Diversity Representation for People Images. FAccT (2024)
Candice Schumann, Femi Olanubi, Auriel Wright, Ellis Monk Jr., Courtney Heldreth, Susanna Ricco
Consensus and Subjectivity of Skin Tone Annotation for ML Fairness. NeurIPS (2023)
Candice Schumann, Gbolahan O. Olanubi, Auriel Wright, Ellis Monk Jr., Courtney Heldreth, Susanna Ricco
Consensus and Subjectivity of Skin Tone Annotation for ML Fairness. CoRR (2023)
Candice Schumann, Zhi Lang, Nicholas Mattei, John P. Dickerson
Group Fairness in Bandits with Biased Feedback. AAMAS (2022)
Candice Schumann, Susanna Ricco, Utsav Prabhu, Vittorio Ferrari, Caroline Pantofaru
A Step Toward More Inclusive People Annotations for Fairness. CoRR (2021)
Candice Schumann, Susanna Ricco, Utsav Prabhu, Vittorio Ferrari, Caroline Pantofaru
A Step Toward More Inclusive People Annotations for Fairness. AIES (2021)
Debjani Saha, Candice Schumann, Duncan C. McElfresh, John P. Dickerson, Michelle L. Mazurek, Michael Carl Tschantz
Human Comprehension of Fairness in Machine Learning. AIES (2020)
Debjani Saha, Candice Schumann, Duncan C. McElfresh, John P. Dickerson, Michelle L. Mazurek, Michael Carl Tschantz
Measuring Non-Expert Comprehension of Machine Learning Fairness Metrics. ICML (2020)
Debjani Saha, Candice Schumann, Duncan C. McElfresh, John P. Dickerson, Michelle L. Mazurek, Michael Carl Tschantz
Human Comprehension of Fairness in Machine Learning. CoRR (2020)
Candice Schumann, Jeffrey S. Foster, Nicholas Mattei, John P. Dickerson
We Need Fairness and Explainability in Algorithmic Hiring. AAMAS (2020)
Candice Schumann, Xuezhi Wang, Alex Beutel, Jilin Chen, Hai Qian, Ed H. Chi
Transfer of Machine Learning Fairness across Domains. CoRR (2019)
Candice Schumann, Zhi Lang, Nicholas Mattei, John P. Dickerson
Group Fairness in Bandit Arm Selection. CoRR (2019)
Candice Schumann, Samsara N. Counts, Jeffrey S. Foster, John P. Dickerson
The Diverse Cohort Selection Problem. AAMAS (2019)
Candice Schumann, Zhi Lang, Jeffrey S. Foster, John P. Dickerson
Making the Cut: A Bandit-based Approach to Tiered Interviewing. CoRR (2019)
Candice Schumann, Zhi Lang, Jeffrey S. Foster, John P. Dickerson
Making the Cut: A Bandit-based Approach to Tiered Interviewing. NeurIPS (2019)
Candice Schumann, Samsara N. Counts, Jeffrey S. Foster, John P. Dickerson
The Diverse Cohort Selection Problem: Multi-Armed Bandits with Varied Pulls. CoRR (2017)