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

This chapter introduces a three-factor responsibility framework — Data × Algorithms × People — for analyzing who (or what) drives online “filter bubbles.” Rather than blaming math alone, we show how outcomes emerge from the interactions of training data, algorithmic logic, and human behavior. A custom simulation of 500 iterations across 2×2 experimental conditions (overlapping vs. polarized preferences × deterministic vs. stochastic collaborative filtering) reveals that the same tweak can either widen or shrink ideological overlap depending on context. The chapter situates these results in current U.S. legislation debates, including the Algorithmic Accountability Act (AAA) and Filter Bubble Transparency Act (FBTA).

Highlighted state of scientific literature on filter bubbles that, when considered comprehensively, shows that the filter bubble is not a simple problem of algorithmic bias. We introduced a framework and demonstrated how existing evidence reinforces the idea that media environments are complex interactions of data, algorithms, and human behavior.

Built an experimental 2x2 simulation contrasting polarized vs overlapping markets and classic vs stochastic recommenders. Results show how the same algorithmic change can either widen or shrink ideological overlap depending on the interaction between data and user behavior.

The chapter argues that the current policy debate around filter bubbles is too focused simplistic memes around "transparency", "bias", and "accountability". We call for a more nuanced, contextually-dependent, and empirically informed understanding of the interplay between data, algorithms, and human behavior to effectively address the issue.