Ethical Dimensions of Music Information Retrieval Technology

Abstract

This article examines ethical dimensions of Music Information Retrieval (MIR) technology. It uses practical ethics (especially computer ethics and engineering ethics) and socio-technical approaches to provide a theoretical basis that can inform discussions of ethics in MIR. To help ground the discussion, the article engages with concrete examples and discourse drawn from the MIR field. This article argues that MIR technology is not value-neutral but is influenced by design choices, and so has unintended and ethically relevant implications. These can be invisible unless one considers how the technology relates to wider society. The article points to the blurring of boundaries between music and technology, and frames music as "informationally enriched" and as a "total social fact." The article calls attention to biases that are introduced by algorithms and data used for MIR technology, cultural issues related to copyright, and ethical problems in MIR as a scientific practice. The article concludes with tentative ethical guidelines for MIR developers, and calls for addressing key ethical problems with MIR technology and practice, especially those related to forms of bias and the remoteness of the technology development from end users.

Artificial Intelligence and Music: Open Questions of Copyright Law and Engineering Praxis

“…Technology is a double-edged sword, with benefits and detriments that deserve to be critically analysed as it is developed, applied, improved and retired. General-purpose technologies such as AI that have the potential to affect economies and societies require study from broad and diverse perspectives, with considerations of questions that touch on law, economy, ecology, and philosophy (Bostrom and Yudkowsky 2014;Craglia 2018;Dusek 2006;Holzapfel et al 2018). Relevant initiatives in ML research are centred on developing AI systems that are not just accurate, but also fair, accountable, transparent and interpretable.…”

“…The second perspective is engineering praxis, which arises from concerns as to the impact of AI technology on music ecosystems and the ways in which such engineers work. Many questions are raised here as well that cannot be answered, but what is clear is that music AI should be developed and used in consultation with music practitioners (Holzapfel et al 2018;.…”

“…Working in these ways can help address issues related to the socio-economic impact of AI systems, define new evaluation principles centred around these issues, and prevent and mitigate negative effects, e.g., see Benthall and Haynes (2019). Discussions about bias, fairness and transparency in the development and application of AI to music are just beginning, e.g., Holzapfel et al (2018). 40 Music creation is a part of a landscape in which various AI-driven tools are deployed and used for different purposes, e.g., composing, mixing, and streaming.…”

“…What happens in such a case? These questions have yet to be answered, but they must be addressed continually, and with the collaboration of practitioners as such research progresses (Holzapfel et al 2018).…”

Diversifying MIR: Knowledge and Real-World Challenges, and New Interdisciplinary Futures

“…4 Evidence of gender imbalances in MIR is provided by Hu et al (2016), who also describe the organisational response: the creation at the 2011 ISMIR conference of 'Women in MIR' or WiMIR sessions 'in order to identify current issues and challenges female MIR researchers face, and to brainstorm ideas for providing more support to female MIR researchers' (Hu et al, 2016: 765). A complementary perspective comes from a wide-ranging, reflexive discussion of 'Ethical dimensions of MIR' by Holzapfel, Sturm and Coeckelbergh (2018), which identifies bias in several senses. Among them are demographic biases stemming from the fact that the MIR community, ' as many engineering research communities', is characterised by researchers who ' are typically WEIRD (white, educated, industrialized, rich, operating within democracies) (Henrich et al, 2010), from a limited set of geographical origins, and a majority is male' (Holzapfel et al, 2018: 50).…”