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Controlling the Conversation: The Ethics of Social Platforms and Content Moderation
With social platforms’ prevailing dominance, there are numerous debates around who owns information, content, and the audience itself: the publisher, or the platform where the content is discovered—or not discovered, as the case may be. Platforms rely heavily on algorithms to decide what to surface to their users across the globe, and they also rely on algorithms to decide what content is taken down. Meanwhile, publishers are making similar decisions on a significantly smaller scale, and not necessarily algorithmically or quite as generically. But how are any of these decisions made? And what are the various factors taken into account to ensure that the decision-making is fair and ethical?
On February 23, 2018, the Tow Center for Digital Journalism at Columbia University and the Annenberg Innovation Lab at USC Annenberg School for Communication and Journalism hosted a Policy Exchange Forum followed by a conference on the topic of “Controlling the Conversation: The Ethics of Social Platforms and Content.”
The Policy Exchange Forum was a closed-group discussion that followed the Chatham House Rule. The discussion broadly focused on three topics: “Ethics of Moderation”, “Moderation Tools”, and “Technological Challenges.
Chasm in Hegemony: Explaining and Reproducing Disparities in Homophilous Networks
In networks with a minority and a majority community, it is well-studied that
minorities are under-represented at the top of the social hierarchy. However,
researchers are less clear about the representation of minorities from the
lower levels of the hierarchy, where other disadvantages or vulnerabilities may
exist. We offer a more complete picture of social disparities at each social
level with empirical evidence that the minority representation exhibits two
opposite phases: at the higher rungs of the social ladder, the representation
of the minority community decreases; but, lower in the ladder, which is more
populous, as you ascend, the representation of the minority community improves.
We refer to this opposing phenomenon between the upper-level and lower-level as
the \emph{chasm effect}. Previous models of network growth with homophily fail
to detect and explain the presence of this chasm effect. We analyze the
interactions among a few well-observed network-growing mechanisms with a simple
model to reveal the sufficient and necessary conditions for both phases in the
chasm effect to occur. By generalizing the simple model naturally, we present a
complete bi-affiliation bipartite network-growth model that could successfully
capture disparities at all social levels and reproduce real social networks.
Finally, we illustrate that addressing the chasm effect can create fairer
systems with two applications in advertisement and fact-checks, thereby
demonstrating the potential impact of the chasm effect on the future research
of minority-majority disparities and fair algorithms