  Most media content consumed today is provided by digital platforms that
aggregate input from diverse sources, where access to information is mediated
by recommendation algorithms. One principal challenge in this context is
dealing with content that is considered harmful. Striking a balance between
competing stakeholder interests, rather than block harmful content altogether,
one approach is to minimize the exposure to such content that is induced
specifically by algorithmic recommendations. Hence, modeling media items and
recommendations as a directed graph, we study the problem of reducing the
exposure to harmful content via edge rewiring. We formalize this problem using
absorbing random walks, and prove that it is NP-hard and NP-hard to approximate
to within an additive error, while under realistic assumptions, the greedy
method yields a (1-1/e)-approximation. Thus, we introduce Gamine, a fast greedy
algorithm that can reduce the exposure to harmful content with or without
quality constraints on recommendations. By performing just 100 rewirings on
YouTube graphs with several hundred thousand edges, Gamine reduces the initial
exposure by 50%, while ensuring that its recommendations are at most 5% less
relevant than the original recommendations. Through extensive experiments on
synthetic data and real-world data from video recommendation and news feed
applications, we confirm the effectiveness, robustness, and efficiency of
Gamine in practice.
