17 citations · 22 across the 3 of their papers we have counts for
8 papers
Distributionally Robust Survival Analysis: A Novel Fairness Loss Without Demographics
Shu Hu, George H. Chen
We propose a general approach for training survival analysis models that minimizes a worst-case error across all subpopulations that are large enough (occurring with at least a use…
Characterizing Fairness Over the Set of Good Models Under Selective Labels
Amanda Coston, Ashesh Rambachan, Alexandra Chouldechova
Algorithmic risk assessments are used to inform decisions in a wide variety of high-stakes settings. Often multiple predictive models deliver similar overall performance but differ…
Leveraging Administrative Data for Bias Audits: Assessing Disparate Coverage with Mobility Data for COVID-19 Policy
Amanda Coston, Neel Guha, Derek Ouyang +3
Anonymized smartphone-based mobility data has been widely adopted in devising and evaluating COVID-19 response strategies such as the targeting of public health resources. Yet litt…
Counterfactual Predictions under Runtime Confounding
Amanda Coston, Edward H. Kennedy, Alexandra Chouldechova
Algorithms are commonly used to predict outcomes under a particular decision or intervention, such as predicting whether an offender will succeed on parole if placed under minimal…
Proceedings of NeurIPS 2019 Workshop on Machine Learning for the Developing World: Challenges and Risks of ML4D
Maria De-Arteaga, Tejumade Afonja, Amanda Coston
This is the proceedings of the 3rd ML4D workshop which was help in Vancouver, Canada on December 13, 2019 as part of the Neural Information Processing Systems conference.
Conditional Learning of Fair Representations
Han Zhao, Amanda Coston, Tameem Adel +1
We propose a novel algorithm for learning fair representations that can simultaneously mitigate two notions of disparity among different demographic subgroups in the classification…