Powered by OpenAIRE graph
Found an issue? Give us feedback
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Preprint . 2025
License: CC BY
Data sources: ZENODO
ZENODO
Preprint . 2025
License: CC BY
Data sources: Datacite
ZENODO
Preprint . 2025
License: CC BY
Data sources: Datacite
versions View all 2 versions
addClaim

User Involvement in Recommender System Audits: A Systematic Review

Authors: Porcaro, Lorenzo; Macori, Agnese; Raffini, Daniel; Catarci, Tiziana;

User Involvement in Recommender System Audits: A Systematic Review

Abstract

Recommender systems play a pivotal role in shaping access to information online, yet audits of these systems rarely prioritize the perspectives of those most affected by them: the users. In the light of a growing body of work on recommender system audits, this systematic review examines the extent and forms of user involvement, classifying modes of participation and assessing how they influence audit practices. Our analysis finds that most audits treat users primarily as passive data sources, with only a small fraction engaging directly with their lived experiences. Where direct involvement occurs, it provides insights that purely technical approaches often miss, exposing critical blind spots in current methodologies. We argue that meaningful accountability requires a paradigm shift: sustained and active user participation, enhanced access to data for independent auditors, and stronger interdisciplinary collaboration across research, industry, and civil society. These findings highlight the urgent need for auditing frameworks capable of delivering more transparent and genuinely accountable recommender systems.

Keywords

human-computer interaction, interdisciplinary approaches, algorithmic accountability, recommender systems, algorithmic auditing, platform studies

  • BIP!
    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    0
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Funded by