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Found 19 papers, 3 papers with code
Cost-Efficient Continual Learning with Sufficient Exemplar Memory
Continual learning (CL) research typically assumes highly constrained exemplar memory resources.
Leveraging vision-language models for fair facial attribute classification
Performance disparities of image recognition across different demographic populations are known to exist in deep learning-based models, but previous work has largely addressed such fairness problems assuming knowledge of sensitive attribute labels.
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.
Generalization in Healthcare AI: Evaluation of a Clinical Large Language Model
Overall, this study provides new insights for enhancing the deployment of large language models in the societally important domain of healthcare, and improving their performance for broader populations.
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.
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.
A Brief Tutorial on Sample Size Calculations for Fairness Audits
In fairness audits, a standard objective is to detect whether a given algorithm performs substantially differently between subgroups.
Mitigating Urban-Rural Disparities in Contrastive Representation Learning with Satellite Imagery
We propose fair dense representation with contrastive learning (FairDCL) as a method for de-biasing the multi-level latent space of convolution neural network models.
Segmenting across places: The need for fair transfer learning with satellite imagery
This work highlights the need to conduct fairness analysis for satellite imagery segmentation models and motivates the development of methods for fair transfer learning in order not to introduce disparities between places, particularly urban and rural locations.
Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty
Explainability attempts to provide reasons for a machine learning model's behavior to stakeholders.
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.
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.
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.
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.
Using Contextual Information to Improve Blood Glucose Prediction
Person-generated data sources, such as actively contributed surveys as well as passively mined data from social media offer opportunity to capture such context, however the self-reported nature and sparsity of such data mean that such data are noisier and less specific than physiological measures such as blood glucose values themselves.
Deep Landscape Features for Improving Vector-borne Disease Prediction
We then incorporate landscape features from satellite image data from Pakistan, labelled using the CNN, in a well-known Susceptible-Infectious-Recovered epidemic model, alongside dengue case data from 2012-2016 in Pakistan.
From the User to the Medium: Neural Profiling Across Web Communities
Online communities provide a unique way for individuals to access information from those in similar circumstances, which can be critical for health conditions that require daily and personalized management.
Population-aware Hierarchical Bayesian Domain Adaptation
Population attributes are essential in health for understanding who the data represents and precision medicine efforts.
Domain Adaptation for Infection Prediction from Symptoms Based on Data from Different Study Designs and Contexts
Acute respiratory infections have epidemic and pandemic potential and thus are being studied worldwide, albeit in many different contexts and study formats.
