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pro vyhledávání: '"Martin, JR"'
Understanding the long-term impact of algorithmic interventions on society is vital to achieving responsible AI. Traditional evaluation strategies often fall short due to the complex, adaptive and dynamic nature of society. While reinforcement learni
Externí odkaz:
http://arxiv.org/abs/2310.12494
Autor:
Martin, Jr., Donald, Kinney, David
Deep learning is a powerful set of techniques for detecting complex patterns in data. However, when the causal structure of that process is underspecified, deep learning models can be brittle, lacking robustness to shifts in the distribution of the d
Externí odkaz:
http://arxiv.org/abs/2309.10211
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
Publikováno v:
Alifmatika, Vol 6, Iss 1, Pp 1-13 (2024)
This research examines the influence of parental involvement tactics on students' mathematics homework completion rates, aimed at pinpointing crucial factors and evaluating the efficacy of such strategies. Utilizing a qualitative-quantitative approac
Externí odkaz:
https://doaj.org/article/ade52ccb9ee442c88af6f9dada3bf169
The Digital Age has changed everything. Mental illness is nothing like what it was even twenty years ago. Since the advent of the Internet, suicide rates have soared. Depression has become the single most debilitating disease in the world. The majori
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:
Joshua Bloom, Waldo E. Martin Jr
This timely special edition, published on the fiftieth anniversary of the founding of the Black Panther Party, features a new preface by the authors that places the Party in a contemporary political landscape, especially as it relates to Black Lives