Algorithmic Auditing for Music Discoverability

The evolution of online platforms over the past decades has radically transformed the way people discover music, and nowadays thanks to social media and music streaming services listeners have access to an ever-increasing amount of tracks and artists.

Within these platforms, one of the goals of Recommender Systems (RSs) is to help users discover music without making them feel overwhelmed while exploring the huge catalogues available.

However, RSs have come under scrutiny from the scientific community, policy-makers, and civil society due to their potential negative societal impact, notably with regard to issues of fairness, non-discrimination, inclusion and diversity.

Algorithmic auditing has emerged as a tool to analyse the problematic behaviours exhibited by RSs, and to offer remedies that can limit their negative impact.

The AA4MD project aims to advance this area of research by crafting auditing techniques tailored specifically for music RSs.

The objective of the project is to demonstrate how the involvement of end-users in the auditing process can contribute to the recognition, analysis, and mitigation of problematic behaviours which may arise while discovering music.

The focus will be on highlighting how RSs, by influencing the discoverability of music, can impact listeners' exposure to culturally diverse content.

In essence, AA4MD will provide 1) qualitative and quantitative insights into how users experience problematic behaviours of RSs when discovering music, 2) a web-based auditing tool that enables large-scale audits, developed following human-centred design practices, and 3) recommendations for policy-makers to enhance the discoverability of culturally diverse content on online platforms.

Ultimately, the AA4MD project seeks to contribute to addressing the challenges posed by music RSs' problematic behaviours and to foster a more inclusive and diverse environment for music discovery within the digital landscape.