Algorithmic Auditing for
Music Discoverability
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 AA4MD project seeks to addressing the challenges posed by music recommenders' problematic behaviours and to foster a more inclusive and diverse environment for online music discovery.
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
The goal 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.
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.
This project is led by Lorenzo Porcaro, Postdoctoral Fellow at the Department of Computer, Control and Management Engineering "A. Ruberti", Sapienza University of Rome.
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
If you would like to learn more about the project, you can start by exploring the research roadmap we presented at the 2nd Music Recommender Systems Workshop 2024, co-located with the 18th ACM Conference on Recommender Systems (RecSys 2024):Porcaro, L., Gómez, E., & Catarci, T. (2024). End-user Algorithmic Auditing for Music Discoverability: A Research RoadmapIf you are interested in understanding the different ways users can be involved in recommender system audits, you can refer to this preprint:Porcaro, L., Macori, A., Raffini, D., & Catarci, T. (2025). User Involvement in Recommender System Audits: A Systematic Review. Zenodo. https://doi.org/10.5281/zenodo.17641849You can also find our first results based on interviews with Italian young adult music listeners, where we investigate in depth the kinds of harmful and problematic behaviors that may affect music discoverability:Porcaro, L., Mirabella, V., Gomez, E., & Catarci, T. (2026). Surfacing Problematic Recommender System Behaviors Affecting Music Discoverability: A Think-Aloud Protocol. ACM CHI conference on Human Factors in Computing Systems (CHI'26), Barcelona, Spain. ACM. https://doi.org/10.1145/3772318.3791406But that's not all! A recent pre-AA4MD study, where several hypotheses that informed the AA4MD proposal were first developed, has been published. In this work, we use a psychosocial methodology (Emotional Textual Analysis) to explore which cultural repertoires are most prominent among Italian music listeners:Porcaro, L., & Monaldi, C. (2026) Recommender systems, representativeness, and online music: a psychosocial analysis of Italian listeners. Humanities & Social Sciences Communications. https://doi.org/10.1057/s41599-026-07044-y
20/04/2026: Panteia-led consortium & DG EAC (Brussels, BE)
Conference on the Discoverability of Diverse European Cultural Content in the Digital Environment - Panel 1: Platforms, Algorithms and Cultural Choice13-17/04/2026: ACM CHI conference on Human Factors in Computing System (CHI2026, Barcelona, ES)
"AI Personality" session - "Surfacing Problematic Recommender System Behaviors Affecting Music Discoverability: A Think-Aloud Protocol "19/02/2026: imec-SMIT (Studies in Media, Innovation & Technology), Vrije Universiteit Brussel (Brussels, BE)
"Listening to Listeners: End-User Auditing of (Music) Recommender Systems"18/02/2026: Reclaiming fairness in Europe’s digital music ecosystem: Fair MusE final conference (Brussels, BE)
"Session 2 – Transparency, data and algorithmic assessment"20/11/2025: LINECHECK 2025 Festival (Milan, IT)
"Is All Diversity Created Equal"? Reckoning with Representation in Music Law and Practice8/11/2025: Mozilla Festival 2025 (Barcelona, ES)
Reclaiming Music Discovery from Recommender Systems29/10/2025 & 6/11/2025: Interuniversitäre Einrichtung Wissenschaft & Kunst (Salzburg, DE) & Music Technology Group, UPF (Barcelona, ES)
Algorithmic Auditing for Music Discoverability: Engaging Listeners to Broaden Cultural Diversity in Recommender Systems (link 1, link 2)11/07/2025: Brisa Studio (Malaga, ES)
Hacking the algorithms: How to leverage online music platforms29/04/2025 & 08/07/2025: UXLab DigiLab, Sapienza University of Rome (Rome, IT) & Institute of Cognitive Sciences and Technologies (ISTC), CNR (Rome, IT)
Algorithmic Auditing for Music Discoverability (AA4MD)