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

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.