Zobrazeno 1 - 10
of 24
pro vyhledávání: '"Mhasawade, Vishwali"'
Autor:
Mhasawade, Vishwali, Chunara, Rumi
Transported mediation effects provide an avenue to understand how upstream interventions (such as improved neighborhood conditions like green spaces) would work differently when applied to different populations as a result of factors that mediate the
Externí odkaz:
http://arxiv.org/abs/2403.08638
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. An important example is street view imagery, available in over 100 coun
Externí odkaz:
http://arxiv.org/abs/2402.06059
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 explan
Externí odkaz:
http://arxiv.org/abs/2401.14539
Autor:
Mhasawade, Vishwali, Chunara, Rumi
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 primarily focused on addressing discrimination due to individually linked att
Externí odkaz:
http://arxiv.org/abs/2010.07343
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. We present here a very brief introdu
Externí odkaz:
http://arxiv.org/abs/2008.07278
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 important mandate for responsible deployment of models. The domain adaptatio
Externí odkaz:
http://arxiv.org/abs/1911.00677
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 another due to variability in features; even within disease labels the
Externí odkaz:
http://arxiv.org/abs/1908.09222
Population attributes are essential in health for understanding who the data represents and precision medicine efforts. Even within disease infection labels, patients can exhibit significant variability; "fever" may mean something different when repo
Externí odkaz:
http://arxiv.org/abs/1811.08579
All known natural language determiners are conservative. Psycholinguistic experiments indicate that children exhibit a corresponding learnability bias when faced with the task of learning new determiners. However, recent work indicates that this bias
Externí odkaz:
http://arxiv.org/abs/1809.05733
Publikováno v:
Proceedings of the National Academy of Sciences of the United States of America; 9/24/2024, Vol. 121 Issue 39, p1-3, 5p
