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The rise of “filter bubbles,” which narrow the scope of users’ information environments, is one of the most concerning unanticipated consequences of the Internet. The question, however, is how these bubbles of polarization arise. Are they inherent in algorithmic filtering mechanisms such as recommendation engines, or do they arise from other causes as well? All algorithmic systems can be understood in terms of three elements: data, algorithmic logic, and human interactions. Abstracting in this way avoids getting caught up in the complexity and variety of data science techniques. It also counterbalances the natural tendency to focus solely on algorithms themselves. While services such as Facebook contribute to filter bubbles by algorithmically recommending content that reinforces existing viewpoints, what users share to begin with and what they click on once surfaced by the algorithm also matter a great deal. A simulation experiment demonstrates how filter bubbles emerge or collapse from the interactions of all three factors.

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4 - Who Do We Blame for the Filter Bubble? On the Roles of Math, Data, and People in Algorithmic Social Systems

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4 - Who Do We Blame for the Filter Bubble? On the Roles of Math, Data, and People in Algorithmic Social Systems

Summary