Discovery Early Career Researcher Award - Grant ID: DE200101253
Funder
Australian Research Council
Funding Amount
$349,586.00
Summary
Making Machine Learning Fair(er). This project aims to develop and implement statistical methods to fight against algorithm bias. In doing so, this project expects to generate new knowledge in the mathematical sciences by employing innovative and interdisciplinary approaches to the development of fairness constraints on machine learning algorithms. Fairness will be seen through the lens of invariance, allowing the developed conceptual framework to find broad applications. Expected outcomes of t ....Making Machine Learning Fair(er). This project aims to develop and implement statistical methods to fight against algorithm bias. In doing so, this project expects to generate new knowledge in the mathematical sciences by employing innovative and interdisciplinary approaches to the development of fairness constraints on machine learning algorithms. Fairness will be seen through the lens of invariance, allowing the developed conceptual framework to find broad applications. Expected outcomes of this project include improved techniques for imposing invariance on deep learning algorithms. This should provide significant benefits to the general public by contributing to the advancement of socially responsible and conscientious machine learning.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE180100203
Funder
Australian Research Council
Funding Amount
$348,575.00
Summary
Deep space-time models for modelling complex environmental phenomena. This project aims to adapt deep-learning models, used in areas of artificial intelligence such as image tagging and automatic text translation, to improve our understanding of the environment. The project expects to develop new theory for deep-learning models to learn from measurement data and numerical-model output about environmental phenomena that evolve in space and time, such as ice sheets and the atmosphere. Expected out ....Deep space-time models for modelling complex environmental phenomena. This project aims to adapt deep-learning models, used in areas of artificial intelligence such as image tagging and automatic text translation, to improve our understanding of the environment. The project expects to develop new theory for deep-learning models to learn from measurement data and numerical-model output about environmental phenomena that evolve in space and time, such as ice sheets and the atmosphere. Expected outcomes include the ability to provide reliable predictions and quantification of uncertainty on environmental concerns of national importance, such as sea-level rise. Key benefits include improved risk management and mitigation, for example through financial incentives or infrastructure planning.Read moreRead less