Polarization is implicated in the erosion of democracy and the progression to
violence, which makes the polarization properties of large algorithmic content
selection systems (recommender systems) a matter of concern for peace and
security. While algorithm-driven social media does not seem to be a primary
driver of polarization at the country level, it could be a useful intervention
point in polarized societies. This paper examines algorithmic depolarization
interventions with the goal of conflict transformation: not suppressing or
eliminating conflict but moving towards more constructive conflict. Algorithmic
intervention is considered at three stages: which content is available
(moderation), how content is selected and personalized (ranking), and content
presentation and controls (user interface). Empirical studies of online
conflict suggest that the exposure diversity intervention proposed as an
antidote to "filter bubbles" can be improved and can even worsen polarization
under some conditions. Using civility metrics in conjunction with diversity in
content selection may be more effective. However, diversity-based interventions
have not been tested at scale and may not work in the diverse and dynamic
contexts of real platforms. Instead, intervening in platform polarization
dynamics will likely require continuous monitoring of polarization metrics,
such as the widely used "feeling thermometer." These metrics can be used to
evaluate product features, and potentially engineered as algorithmic
objectives. It may further prove necessary to include polarization measures in
the objective functions of recommender algorithms to prevent optimization
processes from creating conflict as a side effect.

本文探讨了三个阶段的算法干预方案，重点在于如何利用推荐系统来解决分裂和冲突问题，以达到更具建设性的冲突转型。实证研究表明，基于多样性的干预措施可能会在某些情况下加剧分裂。因此，本文建议将文本中的温和性度量与多样性相结合，以制定更有效的干预措施。