Antitrust, Amazon, and Algorithmic Auditing
Abhisek Dash, Abhijnan Chakraborty, Saptarshi Ghosh, Animesh Mukherjee, Jens Frankenreiter, Stefan Bechtold, Krishna P. Gummadi
TL;DR
The paper investigates self-preferencing in digital platforms through Amazon as a concrete test case, arguing that algorithmic auditing and large-scale empirical methods can illuminate how choice architectures influence consumer and market outcomes. It presents four detailed case studies—Buy Box, offer listings, Alexa voice search, and related-item recommendations—combined with consumer surveys to quantify potential biases and misalignment with stated preferences. The findings reveal systematic tendencies toward preferential treatment for Amazon-affiliated sellers and products, though the authors caution that such effects are context-specific and not a universal legal conclusion. The work emphasizes ex-ante regulatory design, data-access provisions, and the need for cross-disciplinary methods to monitor and regulate platform markets at scale, suggesting a broader role for algorithmic auditing in digital regulation.
Abstract
In digital markets, antitrust law and special regulations aim to ensure that markets remain competitive despite the dominating role that digital platforms play today in everyone's life. Unlike traditional markets, market participant behavior is easily observable in these markets. We present a series of empirical investigations into the extent to which Amazon engages in practices that are typically described as self-preferencing. We discuss how the computer science tools used in this paper can be used in a regulatory environment that is based on algorithmic auditing and requires regulating digital markets at scale.