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Found 9 papers, 1 papers with code
Population-aware Hierarchical Bayesian Domain Adaptation via Multiple-component Invariant Learning
Based on sources of stability in the model, we posit that for human-sourced data and health prediction tasks we can combine environment and population information in a novel population-aware hierarchical Bayesian domain adaptation framework that harnesses multiple invariant components through population attributes when needed.
Neural Networks and Quantifier Conservativity: Does Data Distribution Affect Learnability?
In this work, we investigate whether the learnability bias exhibited by children is in part due to the distribution of quantifiers in natural language.
Population-aware Hierarchical Bayesian Domain Adaptation
Population attributes are essential in health for understanding who the data represents and precision medicine efforts.
Fairness Violations and Mitigation under Covariate Shift
We study the problem of learning fair prediction models for unseen test sets distributed differently from the train set.
Machine Learning in Population and Public Health
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 individuals, but communities as a whole.
Causal Multi-Level Fairness
While work in algorithmic fairness to-date has primarily focused on addressing discrimination due to individually linked attributes, social science research elucidates how some properties we link to individuals can be conceptualized as having causes at macro (e. g. structural) levels, and it may be important to be fair to attributes at multiple levels.
Understanding Disparities in Post Hoc Machine Learning Explanation
Previous work has highlighted that existing post-hoc explanation methods exhibit disparities in explanation fidelity (across 'race' and 'gender' as sensitive attributes), and while a large body of work focuses on mitigating these issues at the explanation metric level, the role of the data generating process and black box model in relation to explanation disparities remains largely unexplored.
Impact on Public Health Decision Making by Utilizing Big Data Without Domain Knowledge
Relevant to such uses, important examples of bias in the use of AI are evident when decision-making based on data fails to account for the robustness of the data, or predictions are based on spurious correlations.
Disparate Effect Of Missing Mediators On Transportability of Causal Effects
We study this issue of missing mediators, motivated by challenges in public health, wherein mediators can be missing, not at random.
