[T6] 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.

The slides shown in this tutorial are available through this link.



Andre Holzapfel received M.Sc. and Ph.D. degrees in computer science from the University of Crete, Greece, and a second Ph.D. degree in music from the Centre of Advanced Music Studies (MIAM) in Istanbul, Turkey. He worked at several leading institutes in computer engineering as postdoctoral researcher, with a focus on rhythm analysis in music information retrieval. His field work in ethnomusicology was mainly conducted in Greece, with Cretan dance being the subject of his second dissertation. In 2016, he became Assistant Professor in Media Technology at the KTH Royal Institute of Technology in Stockholm, Sweden. Since then, his research subjects incorporate the computational analysis of human rhythmic behavior by means of sensor technology, and the investigation of ethical aspects of computational approaches to music.

Marius Miron is a Postdoctoral researcher for European Commission’s Joint Research Centre within the project HUMAINT, working on fairness and interpretable machine learning and on assessing the influence of artificial intelligence on humans. He has a PhD (2018) in Computer Science (Audio Signal Processing and Machine Learning) from Pompeu Fabra University, Barcelona. His PhD thesis concerned separating the audio corresponding to the instruments in an orchestral music mixture. He has completed internships at Computational Perception Group, Johannes Kepler University, Linz where he worked on deep learning for source separation, and at Telefonica Research, Barcelona, where he worked on catastrophic forgetting in machine learning. During 2011-2013 he was a research engineer for the research institute INESC in Porto for a project aiming at modelling groove in music.

Bob L. Sturm received the B.A. degree in physics from University of Colorado, Boulder in 1998, the M.A. degree in Music, Science, and Technology, at Stanford University, in 1999, the M.S. degree in multimedia engineering in the Media Arts and Technology program at University of California, Santa Barbara (UCSB), in 2004, and the M.S. and Ph.D. degrees in Electrical and Computer Engineering at UCSB, in 2007 and 2009. In Dec. 2014, he became a Lecturer at the Centre for Digital Music at Queen Mary University of London. In July 2018 he became an associate professor of computer science at the Royal Institute of Technology KTH in Stockholm Sweden.

Emilia Gómez leads the HUMAINT (HUman and MAchine INTelligence) team at the Centre for Advanced Studies, Joint Research Centre (European Commission) and the MIR (Music Information Research) lab of the Music Technology Group, Universitat Pompeu Fabra in Barcelona. Her research background is in music information retrieval, where she has particularly focused on pitch, melody and tonal processing of music audio signals. She also researches more widely on the impact of artificial intelligence technologies on human behaviour. She is a Telecommunication Engineer (Universidad de Sevilla, Spain), DEA in Acoustics, Signal Processing and Computer Science applied to Music (IRCAM, Paris) and Ph.D. in Computer Science (Universitat Pompeu Fabra). Emilia Gómez has co-authored more than 130 scientific publications in peer-reviewed scientific journals and conferences, and contributed to several open datasets and software libraries. She is currently president of the International Society for Music Information Retrieval (ISMIR), and particularly interested in promoting diversity in the MIR field.