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Biases in Recommendation System

Recommendation systems shape what people consume and experience online, which makes it critical to assess their effect on society and whether they are affected by any potential source of bias. My research focuses on a specific source of bias — popularity — that is especially relevant in two online contexts: news consumption, and cultural markets. Social media newsfeeds, which are designed to optimize the engagement of users, may inadvertently promote inaccurate and partisan news domains, since these are often popular among like-minded and polarized audiences. More generally, in any type of cultural market, popularity bias may lead to the suppression of niche quality products, which may not attract enough attention. In the context of online newsfeeds, I am investigating whether the political diversity of the audience of a news website can be used as signal of journalistic quality. In an analysis of a comprehensive dataset of news source reliability ratings and web browsing histories, I have shown that the diversity of the audience of a news website is a valuable signal to counter popularity bias and to promote journalistic quality. To further validate these results experimentally, I propose to have direct interactions with social media users through surveys. Here, I provide the details of a field study that I am planning to undertake using an experimental social media platform. More generally, in the context of any online cultural market, political audience diversity may not be applicable but the idea of diversity as a signal for higher quality might still be useful. For example, movies which are liked by an “age diverse” population or books which are read by racially diverse audience may have better quality than other items. However, in this research I have explored audience diversity in the context of social media newsfeeds only. For any online cultural market, I propose a method to quantify the popularity bias through an empirical analysis, using data from several existing markets. Accurately estimating the impact of popularity bias can help us advance our understanding of machine biases and also lead to the development of more robust recommendation systems.