39 citations · 49 across the 10 of their papers we have counts for
11 papers
Understanding challenges to the interpretation of disaggregated evaluations of algorithmic fairness
Stephen R. Pfohl, Natalie Harris, Chirag Nagpal +12
Disaggregated evaluation across subgroups is critical for assessing the fairness of machine learning models, but its uncritical use can mislead practitioners. We show that equal pe…
Machine Learning for Health symposium 2024 -- Findings track
Stefan Hegselmann, Helen Zhou, Elizabeth Healey +6
A collection of the accepted Findings papers that were presented at the 4th Machine Learning for Health symposium (ML4H 2024), which was held on December 15-16, 2024, in Vancouver,…
Disparate Effect Of Missing Mediators On Transportability of Causal Effects
Vishwali Mhasawade, Rumi Chunara
Transported mediation effects provide an avenue to understand how upstream interventions (such as improved neighborhood conditions like green spaces) would work differently when ap…
Impact on Public Health Decision Making by Utilizing Big Data Without Domain Knowledge
Miao Zhang, Salman Rahman, Vishwali Mhasawade +1
New data sources, and artificial intelligence (AI) methods to extract information from them are becoming plentiful, and relevant to decision making in many societal applications. A…
Understanding Disparities in Post Hoc Machine Learning Explanation
Vishwali Mhasawade, Salman Rahman, Zoe Haskell-Craig +1
Previous work has highlighted that existing post-hoc explanation methods exhibit disparities in explanation fidelity (across 'race' and 'gender' as sensitive attributes), and while…
Causal Multi-Level Fairness
Vishwali Mhasawade, Rumi Chunara
Algorithmic systems are known to impact marginalized groups severely, and more so, if all sources of bias are not considered. While work in algorithmic fairness to-date has primari…
Machine Learning in Population and Public Health
Vishwali Mhasawade, Yuan Zhao, Rumi Chunara
Research in population and public health focuses on the mechanisms between different cultural, social, and environmental factors and their effect on the health, of not just individ…
Fairness Violations and Mitigation under Covariate Shift
Harvineet Singh, Rina Singh, Vishwali Mhasawade +1
We study the problem of learning fair prediction models for unseen test sets distributed differently from the train set. Stability against changes in data distribution is an import…
Population-aware Hierarchical Bayesian Domain Adaptation via Multiple-component Invariant Learning
Vishwali Mhasawade, Nabeel Abdur Rehman, Rumi Chunara
While machine learning is rapidly being developed and deployed in health settings such as influenza prediction, there are critical challenges in using data from one environment in…
Population-aware Hierarchical Bayesian Domain Adaptation
Vishwali Mhasawade, Nabeel Abdur Rehman, Rumi Chunara
Population attributes are essential in health for understanding who the data represents and precision medicine efforts. Even within disease infection labels, patients can exhibit s…
Neural Networks and Quantifier Conservativity: Does Data Distribution Affect Learnability?
Vishwali Mhasawade, Ildikó Emese Szabó, Melanie Tosik +1
All known natural language determiners are conservative. Psycholinguistic experiments indicate that children exhibit a corresponding learnability bias when faced with the task of l…