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Australian State/Territory : ACT
Research Topic : Simulation and Modelling
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  • Funded Activity

    Australian Laureate Fellowships - Grant ID: FL200100176

    Funder
    Australian Research Council
    Funding Amount
    $3,128,080.00
    Summary
    Theoretical Foundations of Ethical Machine Learning. The project will develop a systematic theory of ethical machine learning. Machine learning is a powerful and pervasive technology that is already having a huge impact on Australia. When applied to data about people there are a range of ethical harms that can arise (fairness, and privacy are two of them). The project will develop a rigorously grounded foundation for managing such ethical harms. For example it will allow the quantification of t .... Theoretical Foundations of Ethical Machine Learning. The project will develop a systematic theory of ethical machine learning. Machine learning is a powerful and pervasive technology that is already having a huge impact on Australia. When applied to data about people there are a range of ethical harms that can arise (fairness, and privacy are two of them). The project will develop a rigorously grounded foundation for managing such ethical harms. For example it will allow the quantification of the inevitable trade-offs between fairness and utility. The benefits of the project will include the best possible ways of managing these trade-offs, competitive advantage for Australian firms developing the technology, and will ensure that the country retains a social license to use the technology.
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    Funded Activity

    Discovery Projects - Grant ID: DP210103877

    Funder
    Australian Research Council
    Funding Amount
    $659,083.00
    Summary
    Theoretical Foundations of Ethical Machine Learning. The project aims to develop a systematic theory of ethical machine learning. Machine learning is a powerful and pervasive technology that is already having a huge impact on Australia. When applied to data about people there are a range of ethical harms that can arise (fairness, and privacy are two of them). The project aims to develop a rigorously grounded foundation for managing such ethical harms. For example it will allow the quantification .... Theoretical Foundations of Ethical Machine Learning. The project aims to develop a systematic theory of ethical machine learning. Machine learning is a powerful and pervasive technology that is already having a huge impact on Australia. When applied to data about people there are a range of ethical harms that can arise (fairness, and privacy are two of them). The project aims to develop a rigorously grounded foundation for managing such ethical harms. For example it will allow the quantification of the inevitable trade-offs between fairness and utility. The benefits of the project should include better ways of managing these trade-offs, a competitive advantage for Australian firms developing the technology, and will ensure that the country retains a social license to use the technology.
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    Funded Activity

    Discovery Early Career Researcher Award - Grant ID: DE200100803

    Funder
    Australian Research Council
    Funding Amount
    $405,763.00
    Summary
    Slicing dead stars to reveal the origin of heavy elements in the Universe. This project aims to improve our understanding of how massive stars forge heavy elements like oxygen, that are key to life. It will use state-of-the-art spectrographs on Australian and Chilean telescopes to observe the ashes of dead stars, and test recent theoretical models. Expected outcomes include spectral maps of young supernova remnants, new observational constraints for theoretical models of massive stars and core-c .... Slicing dead stars to reveal the origin of heavy elements in the Universe. This project aims to improve our understanding of how massive stars forge heavy elements like oxygen, that are key to life. It will use state-of-the-art spectrographs on Australian and Chilean telescopes to observe the ashes of dead stars, and test recent theoretical models. Expected outcomes include spectral maps of young supernova remnants, new observational constraints for theoretical models of massive stars and core-collapse supernovae, and innovative visualization solutions for complex 3D datasets. This project is expected to largely refine our grasp of the formation of heavy elements in the Universe, and thus provide significant cultural benefit in enhancing our understanding of mankind's cosmic origin in the heart of massive stars.
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