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pro vyhledávání: '"Kuhlberg, Jill"'
Much attention and concern has been raised recently about bias and the use of machine learning algorithms in healthcare, especially as it relates to perpetuating racial discrimination and health disparities. Following an initial system dynamics works
Externí odkaz:
http://arxiv.org/abs/2305.13485
Autor:
Martin Jr., Donald, Prabhakaran, Vinodkumar, Kuhlberg, Jill, Smart, Andrew, Isaac, William S.
Machine learning (ML) fairness research tends to focus primarily on mathematically-based interventions on often opaque algorithms or models and/or their immediate inputs and outputs. Such oversimplified mathematical models abstract away the underlyin
Externí odkaz:
http://arxiv.org/abs/2006.09663
Autor:
Martin Jr., Donald, Prabhakaran, Vinodkumar, Kuhlberg, Jill, Smart, Andrew, Isaac, William S.
Recent research on algorithmic fairness has highlighted that the problem formulation phase of ML system development can be a key source of bias that has significant downstream impacts on ML system fairness outcomes. However, very little attention has
Externí odkaz:
http://arxiv.org/abs/2005.07572
Autor:
Naumann, Rebecca B., Sabounchi, Nasim S., Kuhlberg, Jill, Singichetti, Bhavna, Marshall, Stephen W., Hassmiller Lich, Kristen
Publikováno v:
In Accident Analysis and Prevention June 2022 171
Autor:
Langellier, Brent A., Kuhlberg, Jill A., Ballard, Ellis A., Slesinski, S. Claire, Stankov, Ivana, Gouveia, Nelson, Meisel, Jose D., Kroker-Lobos, Maria F., Sarmiento, Olga L., Caiaffa, Waleska Teixeira, Diez Roux, Ana V.
Publikováno v:
In Health and Place November 2019 60
Autor:
Hassmiller Lich, Kristen, author, Kuhlberg, Jill, author
Publikováno v:
Complex Systems and Population Health, 2020, ill.
Externí odkaz:
https://doi.org/10.1093/oso/9780190880743.003.0009
Autor:
Morais, Lidia Maria de Oliveira1 (AUTHOR) lidia.salurbal@gmail.com, Kuhlberg, Jill2 (AUTHOR), Ballard, Ellis2,3 (AUTHOR), Indvik, Katherine4 (AUTHOR), Rocha, Solimar Carnavalli1 (AUTHOR), Sales, Denise Marques1 (AUTHOR), de Oliveira Cardoso, Letícia5 (AUTHOR), Gouveia, Nelson6 (AUTHOR), de Lima Friche, Amélia Augusta1 (AUTHOR), Caiaffa, Waleska Teixeira1 (AUTHOR)
Publikováno v:
Health Research Policy & Systems. 4/1/2021, Vol. 19 Issue 1, p1-15. 15p.
Akademický článek
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Autor:
De Oliveira Morais, Lidia Maria, Kuhlberg, Jill, Ballard, Ellis, Indvik, Katherine, Solimar Carnavalli Rocha, Sales, Denise Marques, De Oliveira Cardoso, Letícia, Gouveia, Nelson, De Lima Friche, Amélia Augusta, Waleska Teixeira Caiaffa
Additional file 1: Questionnaire.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2c18e2a6f2663e23a1323313e55b1cb1
Machine learning (ML) fairness research tends to focus primarily on mathematically-based interventions on often opaque algorithms or models and/or their immediate inputs and outputs. Such oversimplified mathematical models abstract away the underlyin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f994a8ca024c1fbcc0e4e3b80e97684e
http://arxiv.org/abs/2006.09663
http://arxiv.org/abs/2006.09663