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Group Fairness for Content Creators: the Role of Human and Algorithmic Biases under Popularity-based Recommendations

Ionescu, Stefania; Hannak, Aniko; Pagan, Nicolo (2023). Group Fairness for Content Creators: the Role of Human and Algorithmic Biases under Popularity-based Recommendations. In: RecSys '23: 17th ACM Conference on Recommender Systems, Singapore, 18 September 2023 - 22 September 2023. ACM Digital library, 863-870.

Abstract

The Creator Economy faces concerning levels of unfairness. Content creators (CCs) publicly accuse platforms of purposefully reducing the visibility of their content based on protected attributes, while platforms place the blame on viewer biases. Meanwhile, prior work warns about the “rich-get-richer” effect perpetuated by existing popularity biases in recommender systems: Any initial advantage in visibility will likely be exacerbated over time. What remains unclear is how the biases based on protected attributes from platforms and viewers interact and contribute to the observed inequality in the context of popularity-biased recommender systems. The difficulty of the question lies in the complexity and opacity of the system. To overcome this challenge, we design a simple agent-based model (ABM) that unifies the platform systems which allocate the visibility of CCs (e.g., recommender systems, moderation) into a single popularity-based function, which we call the visibility allocation system (VAS). Through simulations, we find that although viewer homophilic biases do alone create inequalities, small levels of additional biases in VAS are more harmful. From the perspective of interventions, our results suggest that (a) attempts to reduce attribute-biases in moderation and recommendations should precede those reducing viewers’ homophilic tendencies, (b) decreasing the popularity-biases in VAS decreases but not eliminates inequalities, (c) boosting the visibility of protected CCs to overcome viewers’ homophily with respect to one fairness metric is unlikely to produce fair outcomes with respect to all metrics, and (d) the process is also unfair for viewers and this unfairness could be overcome through the same interventions. More generally, this work demonstrates the potential of using ABMs to better understand the causes and effects of biases and interventions within complex sociotechnical systems.

Additional indexing

Item Type:Conference or Workshop Item (Paper), refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
08 Research Priority Programs > Digital Society Initiative
Dewey Decimal Classification:000 Computer science, knowledge & systems
Scopus Subject Areas:Physical Sciences > Computer Science Applications
Physical Sciences > Information Systems
Physical Sciences > Software
Physical Sciences > Control and Systems Engineering
Scope:Discipline-based scholarship (basic research)
Language:English
Event End Date:22 September 2023
Deposited On:16 Nov 2023 15:06
Last Modified:20 Jun 2024 10:49
Publisher:ACM Digital library
Series Name:Proceedings of the ACM Conference on Recommender Systems
ISBN:979-8-4007-0241-9
OA Status:Hybrid
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.1145/3604915.3608841
Other Identification Number:merlin-id:24163
Project Information:
  • Funder: SNSF
  • Grant ID: 180545
  • Project Title: NCCR Automation (phase I)
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  • Language: English
  • Licence: Creative Commons: Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)

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