User Attitudes towards Algorithmic Opacity and Transparency in Online Reviewing Platforms

TLDR

Algorithms curate online information but are often opaque, raising concerns about bias and deception, yet little is known about how users perceive such opaque systems. The study investigates which factors shape users’ perceptions of algorithmic opacity and how adding transparency alters their attitudes. Two studies were conducted: a content analysis of 242 users’ online discussions about Yelp’s review‑filtering algorithm and semi‑structured interviews with 15 Yelp users who were informed of the algorithm via a disclosure tool. Users’ trust or criticism of the algorithm depended on their engagement and personal benefit, and transparency shifted attitudes, prompting some to intend to write for the algorithm or to leave the platform.

Abstract

Algorithms exert great power in curating online information, yet are often opaque in their operation, and even existence. Since opaque algorithms sometimes make biased or deceptive decisions, many have called for increased transparency. However, little is known about how users perceive and interact with potentially biased and deceptive opaque algorithms. What factors are associated with these perceptions, and how does adding transparency into algorithmic systems change user attitudes? To address these questions, we conducted two studies: 1) an analysis of 242 users' online discussions about the Yelp review filtering algorithm and 2) an interview study with 15 Yelp users disclosing the algorithm's existence via a tool. We found that users question or defend this algorithm and its opacity depending on their engagement with and personal gain from the algorithm. We also found adding transparency into the algorithm changed users' attitudes towards the algorithm: users reported their intention to either write for the algorithm in future reviews or leave the platform.