Zobrazeno 1 - 10
of 115
pro vyhledávání: '"Ghani, Rayid"'
Autor:
Jesus, Sérgio, Saleiro, Pedro, Silva, Inês Oliveira e, Jorge, Beatriz M., Ribeiro, Rita P., Gama, João, Bizarro, Pedro, Ghani, Rayid
Aequitas Flow is an open-source framework and toolkit for end-to-end Fair Machine Learning (ML) experimentation, and benchmarking in Python. This package fills integration gaps that exist in other fair ML packages. In addition to the existing audit c
Externí odkaz:
http://arxiv.org/abs/2405.05809
Autor:
Vajiac, Catalina, Frey, Arun, Baumann, Joachim, Smith, Abigail, Amarasinghe, Kasun, Lai, Alice, Rodolfa, Kit, Ghani, Rayid
Rental assistance programs provide individuals with financial assistance to prevent housing instabilities caused by evictions and avert homelessness. Since these programs operate under resource constraints, they must decide who to prioritize. Typical
Externí odkaz:
http://arxiv.org/abs/2403.12599
While algorithmic fairness is a thriving area of research, in practice, mitigating issues of bias often gets reduced to enforcing an arbitrarily chosen fairness metric, either by enforcing fairness constraints during the optimization step, post-proce
Externí odkaz:
http://arxiv.org/abs/2309.17337
Autor:
Albright, Thomas D., Darity, William A., Dunn, Diana, Ghani, Rayid, Hayes-Greene, Deena, Hernández, Tanya Katerí, Heron, Sheryl
Publikováno v:
Daedalus, 2024 Jan 01. 153(1), 276-283.
Externí odkaz:
https://www.jstor.org/stable/48763066
Human services systems make key decisions that impact individuals in the society. The U.S. child welfare system makes such decisions, from screening-in hotline reports of suspected abuse or neglect for child protective investigations, placing childre
Externí odkaz:
http://arxiv.org/abs/2207.05855
Autor:
Amarasinghe, Kasun, Rodolfa, Kit T., Jesus, Sérgio, Chen, Valerie, Balayan, Vladimir, Saleiro, Pedro, Bizarro, Pedro, Talwalkar, Ameet, Ghani, Rayid
Most existing evaluations of explainable machine learning (ML) methods rely on simplifying assumptions or proxies that do not reflect real-world use cases; the handful of more robust evaluations on real-world settings have shortcomings in their desig
Externí odkaz:
http://arxiv.org/abs/2206.13503
Autor:
Giles, Oscar, Hosseini, Kasra, Mingas, Grigorios, Strickson, Oliver, Bowler, Louise, Smith, Camila Rangel, Wilde, Harrison, Lim, Jen Ning, Mateen, Bilal, Amarasinghe, Kasun, Ghani, Rayid, Heppenstall, Alison, Lomax, Nik, Malleson, Nick, O'Reilly, Martin, Vollmerteke, Sebastian
Synthetic datasets are often presented as a silver-bullet solution to the problem of privacy-preserving data publishing. However, for many applications, synthetic data has been shown to have limited utility when used to train predictive models. One p
Externí odkaz:
http://arxiv.org/abs/2203.01363
Applications of machine learning (ML) to high-stakes policy settings -- such as education, criminal justice, healthcare, and social service delivery -- have grown rapidly in recent years, sparking important conversations about how to ensure fair outc
Externí odkaz:
http://arxiv.org/abs/2105.06442
Publikováno v:
Nat Mach Intell 3, 896-904 (2021)
Growing use of machine learning in policy and social impact settings have raised concerns for fairness implications, especially for racial minorities. These concerns have generated considerable interest among machine learning and artificial intellige
Externí odkaz:
http://arxiv.org/abs/2012.02972
Publikováno v:
Data & Policy , Volume 5 , 2023 , e5
Explainability is highly-desired in Machine Learning (ML) systems supporting high-stakes policy decisions in areas such as health, criminal justice, education, and employment. While the field of explainable ML has expanded in recent years, much of th
Externí odkaz:
http://arxiv.org/abs/2010.14374