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Fairness, Accountability and Transparency in Music Information Research (FAT-MIR)

Gomez, Emilia; Holzapfel, Andre; Miron, Marius; Sturm, Bob L.

This tutorial focuses on the timely issues of ethics, fairness, accountability and transparency, with particular attention paid to research in applications in music information research. These topics arise from a broader consideration of ethics in the field – related work of which was recently published in TISMIR (https://transactions.ismir.net/articles/10.5334/tismir.13). These topics are also receiving attention in the broader domain of machine learning and data science, e.g., the FAT-Machine Learning (ML) conference 2014-2018, Explainable AI workshops 2017-2018, Interpretable Machine Learning workshops, and in the context of the HUMAINT project and winter school on ethical, legal, social and economic impact of Artificial Intelligence (https://ec.europa.eu/jrc/communities/en/community/humaint). This tutorial is suitable for researchers and students in MIR working in any domain, as these issues are relevant for all MIR tasks, from low- to high-level, from system to user-centered research. There are no prerequisites for taking this tutorial.

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