Auditing recommender systems: safeguarding discoverability of culturally diverse music

Algorithm audits are empirical studies investigating a public algorithmic system for potential problematic behaviour. They have emerged with the explosion of online platforms and AI applications to address the threat of biased outcomes, lack of transparency and misuse of data. With the support of the Marie Skłodowska-Curie Actions programme the Algorithmic Auditing for Music Discoverability (AA4MD) project aims to develop auditing techniques involving end users that are tailored specifically for recommender systems. The project will include the development of a web-based tool for large-scale audits as well as policy recommendations. Its overarching goal is to increase exposure to culturally diverse music.

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 is to help users discover music without making them feel overwhelmed while exploring the huge catalogues available.However, these systems 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 recommender systems, and to offer remedies that can limit their negative impact.

Background

Objectives

The focus will be on highlighting how recommender systems, 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. Web-based auditing tool that enables large-scale audits, developed following human-centred design practices.
3. Recommendations for policy-makers to enhance the discoverability of culturally diverse content on online platforms.

The project Algorithmic Auditing for Music Discoverability (AA4MD) has received funding from the European Union’s Horizon Europe research and innovation programme under the Marie Skłodowska-Curie grant agreement No 101148443.Start date: 01/12/2024
End date: 30/11/2026