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pro vyhledávání: '"Keya, Kamrun Naher"'
Autor:
Wang, Clarice, Wang, Kathryn, Bian, Andrew, Islam, Rashidul, Keya, Kamrun Naher, Foulds, James, Pan, Shimei
Currently, there is a surge of interest in fair Artificial Intelligence (AI) and Machine Learning (ML) research which aims to mitigate discriminatory bias in AI algorithms, e.g. along lines of gender, age, and race. While most research in this domain
Externí odkaz:
http://arxiv.org/abs/2106.07112
Recently, much attention has been paid to the societal impact of AI, especially concerns regarding its fairness. A growing body of research has identified unfair AI systems and proposed methods to debias them, yet many challenges remain. Representati
Externí odkaz:
http://arxiv.org/abs/2104.08769
Healthcare programs such as Medicaid provide crucial services to vulnerable populations, but due to limited resources, many of the individuals who need these services the most languish on waiting lists. Survival models, e.g. the Cox proportional haza
Externí odkaz:
http://arxiv.org/abs/2010.06820
A growing proportion of human interactions are digitized on social media platforms and subjected to algorithmic decision-making, and it has become increasingly important to ensure fair treatment from these algorithms. In this work, we investigate gen
Externí odkaz:
http://arxiv.org/abs/2009.08955
Word embedding models such as the skip-gram learn vector representations of words' semantic relationships, and document embedding models learn similar representations for documents. On the other hand, topic models provide latent representations of th
Externí odkaz:
http://arxiv.org/abs/1909.04702
We propose definitions of fairness in machine learning and artificial intelligence systems that are informed by the framework of intersectionality, a critical lens arising from the Humanities literature which analyzes how interlocking systems of powe
Externí odkaz:
http://arxiv.org/abs/1807.08362
Autor:
Islam, Rashidul1 (AUTHOR) islam.rashidul@umbc.edu, Keya, Kamrun Naher1 (AUTHOR) kkeya1@umbc.edu, Pan, Shimei1 (AUTHOR) shimei@umbc.edu, Sarwate, Anand D.2 (AUTHOR) anand.sarwate@rutgers.edu, Foulds, James R.1 (AUTHOR) jfoulds@umbc.edu
Publikováno v:
Entropy. Apr2023, Vol. 25 Issue 4, p660. 44p.
Autor:
Keya, Kamrun Naher1 (AUTHOR) kkeya1@umbc.edu, Papanikolaou, Yannis2 (AUTHOR) yannis.papanikolaou@healx.io, Foulds, James R.1 (AUTHOR) jfoulds@umbc.edu
Publikováno v:
Computational Linguistics. Dec2022, Vol. 48 Issue 4, p1021-1052. 32p.
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Autor:
Wang, Clarice, Wang, Kathryn, Bian, Andrew, Islam, Rashidul, Keya, Kamrun Naher, Foulds, James R., Pan, Shimei
Mid-Atlantic Student Colloquium on Speech, Language and Learning (MASC-SLL), 2020.
AI is increasingly being used in making consequential decisions such as determining whether someone is granted parole or not (Angwin et al., 2016). Unfortunately,
AI is increasingly being used in making consequential decisions such as determining whether someone is granted parole or not (Angwin et al., 2016). Unfortunately,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d260dd54dc54f44b5dafbbd0b8580560