Publication date
January 2022
DOIAbstract
A talk about FAIR for Machine Learning for the Improving "FAIRness" and "Fairness" of AI/ML in Geoscience session at the 2022 ESIP Winter Meetin
A talk about FAIR for Machine Learning for the Improving "FAIRness" and "Fairness" of AI/ML in Geoscience session at the 2022 ESIP Winter Meetin
We study a fair machine learning (ML) setting where an 'upstream' model developer is tasked with pro...
arXiv admin note: substantial text overlap with arXiv:2001.07864, arXiv:1911.04322, arXiv:1906.05082...
What does it mean for a machine learning model to be ‘fair’, in terms which can be operationalised? ...
The FAIR Guiding Principles aim to improve findability, accessibility, interoperability and reusabil...
A poster at RDA VP16: The idea of FAIR in the context of scientific data management and stewardship...
an invited talk presented at Workshop on Machine Learning Good Practices, co-located with ECCB 202
Machine learning is part of the daily life of people and companies worldwide. Unfortunately, machine...
The FAIR Principles have two aspects: They were written specifically for research data and they also...
Machine learning is part of the daily life of people and companies worldwide. Unfortunately, bias in...
The increasing impact of machine learning and algorithmic decision making on education has brought a...
International audienceFairness emerged as an important requirement to guarantee that Machine Learnin...
A foundational set of findable, accessible, interoperable, and reusable (FAIR) principles were propo...
Machine Learning models are begin increasingly used within the industry such as by financial institu...
A talk in the panel "MetaFAIR — FAIR and Enriched Contextualization of Geoscience Resources" at the ...
Western societies are marked by diverse and extensive biases and inequality that are unavoidably emb...
We study a fair machine learning (ML) setting where an 'upstream' model developer is tasked with pro...
arXiv admin note: substantial text overlap with arXiv:2001.07864, arXiv:1911.04322, arXiv:1906.05082...
What does it mean for a machine learning model to be ‘fair’, in terms which can be operationalised? ...
The FAIR Guiding Principles aim to improve findability, accessibility, interoperability and reusabil...
A poster at RDA VP16: The idea of FAIR in the context of scientific data management and stewardship...
an invited talk presented at Workshop on Machine Learning Good Practices, co-located with ECCB 202
Machine learning is part of the daily life of people and companies worldwide. Unfortunately, machine...
The FAIR Principles have two aspects: They were written specifically for research data and they also...
Machine learning is part of the daily life of people and companies worldwide. Unfortunately, bias in...
The increasing impact of machine learning and algorithmic decision making on education has brought a...
International audienceFairness emerged as an important requirement to guarantee that Machine Learnin...
A foundational set of findable, accessible, interoperable, and reusable (FAIR) principles were propo...
Machine Learning models are begin increasingly used within the industry such as by financial institu...
A talk in the panel "MetaFAIR — FAIR and Enriched Contextualization of Geoscience Resources" at the ...
Western societies are marked by diverse and extensive biases and inequality that are unavoidably emb...
We study a fair machine learning (ML) setting where an 'upstream' model developer is tasked with pro...
arXiv admin note: substantial text overlap with arXiv:2001.07864, arXiv:1911.04322, arXiv:1906.05082...
What does it mean for a machine learning model to be ‘fair’, in terms which can be operationalised? ...
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