How algorithmic popularity bias hinders or promotes quality

By Azadeh Nematzadeh, Giovanni Luca Ciampaglia, Filippo Menczer, Alessandro Flammini. 2017

185 citations recorded by the source corpus.

Algorithms that favor popular items are used to help us select among many choices, from top-ranked search engine results to highly-cited scientific papers. The goal of these algorithms is to identify high-quality items such as reliable news, credible information sources, and important discoveries–in short, high-quality content should rank at the top. Prior work has shown that choosing what is popular may amplify random fluctuations and lead to sub-optimal rankings. Nonetheless, it is often assumed that recommending what is popular will help high-quality content “bubble up” in practice. Here we identify the conditions in which popularity may be a viable proxy for quality content by studying a simple model of a cultural market endowed with an intrinsic notion of quality. A parameter representing the cognitive cost of exploration controls the trade-off between quality and popularity. Below and above a critical exploration cost, popularity bias is more likely to hinder quality. But we find a narrow intermediate regime of user attention where an optimal balance exists: choosing what is popular can help promote high-quality items to the top. These findings clarify the effects of algorithmic popularity bias on quality outcomes, and may inform the design of more principled mechanisms for techno-social cultural markets.

Subfields: Cultural Studies, Statistical and Nonlinear Physics, AI Ethics, Fairness & Society

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In Statistical and Nonlinear Physics

In AI Ethics, Fairness & Society

Citations in the corpus

A Survey on Bias and Fairness in Machine Learning (2019, background): “As an instance, this type of bias can be seen in search engines [61, 96] or recommendation systems where popular objects would be presented more to the public.”
The spread of low-credibility content by social bots (2017, unknown): “These include information overload and finite attention [41], novelty of false news [57], the selective exposure [52, 40, 39] caused by polarized and segregated online social networks [9, 8], algorithmic popularity bias [45, 20, 37], and other cognitive vulnerabilities such as confirmation bias and ”
Towards Intersectionality in Machine Learning: Including More Identities, Handling Underrepresentation, and Performing Evaluation (2022, methodology): “that of simulations as testbeds and tools for understanding sociotechical systems such as in modeling recommender systems [17, 21, 45, 70, 89] and online information diffusion [44, 48, 119], rather than as a source for more data.”