Background Music Curation with Machine Learning

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Abstract

Following the recent outrage towards bias in facial recognition systems, raters, the invisible people who teach, train and validate machine learning powered products, have been put under the spotlight.

With background music curation as an intervention point, this project explores ways for human raters to work with machine learning. By making machine learning explainable, creating handles for human decisions and optimizing the working experience of raters, this project proposes a working model where machine learning collaborates with the raters and augments human intelligence.