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Title: Opening Up the Black Box: Auditing Google’s Top Stories Algorithm
Algorithmic auditing has emerged as an important methodology that gleans insights from opaque platform algorithms. These audits often rely on the repeated observations of an algorithm’s outputs given a fixed set of inputs. For example, to audit Google search, one repeatedly inputs queries and captures the resulting search pages. Then, the goal is to uncover patterns in the data that reveal the “secrets” of algorithmic decision making. In this paper, we introduce one particular algorithm audit, that of Google’s Top stories. We describe the process of data collection, exploration, and analysis for this application and share some of the insights. Concretely, our analysis suggests that Google may be trying to burst the “filter bubble” by choosing less known publishers for the 3rd position in the Top stories. In addition to revealing the behavior of the platform, the audit also illustrated that a subset of publishers cover certain stories more than others.  more » « less
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Proceedings of the ... International Florida Artificial Intelligence Research Society Conference
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National Science Foundation
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