  Individuals of modern societies share ideas and participate in collective
processes within a pervasive, variable, and mostly hidden ecosystem of content
filtering technologies that determine what information we see online. Despite
the impact of these algorithms on daily life and society, little is known about
their effect on information transfer and opinion formation. It is thus unclear
to what extent algorithmic bias has a harmful influence on collective
decision-making, such as a tendency to polarize debate. Here we introduce a
general theoretical framework to systematically link models of opinion
dynamics, social network structure, and content filtering. We showcase the
flexibility of our framework by exploring a family of binary-state opinion
dynamics models where information exchange lies in a spectrum from pairwise to
group interactions. All models show an opinion polarization regime driven by
algorithmic bias and modular network structure. The role of content filtering
is, however, surprisingly nuanced; for pairwise interactions it leads to
polarization, while for group interactions it promotes coexistence of opinions.
This allows us to pinpoint which social interactions are robust against
algorithmic bias, and which ones are susceptible to bias-enhanced opinion
polarization. Our framework gives theoretical ground for the development of
heuristics to tackle harmful effects of online bias, such as information
bottlenecks, echo chambers, and opinion radicalization.
