Minorities in networks and algorithms
It addresses societal inequality and marginalization for minorities, but is incremental as it synthesizes existing research rather than presenting new findings.
This chapter reviews how social network models and algorithms can reveal and potentially address societal inequalities affecting minorities, examining factors like homophily and algorithmic biases that shape visibility and collaboration patterns.
In this chapter, we provide an overview of recent advances in data-driven and theory-informed complex models of social networks and their potential in understanding societal inequalities and marginalization. We focus on inequalities arising from networks and network-based algorithms and how they affect minorities. In particular, we examine how homophily and mixing biases shape large and small social networks, influence perception of minorities, and affect collaboration patterns. We also discuss dynamical processes on and of networks and the formation of norms and health inequalities. Additionally, we argue that network modeling is paramount for unveiling the effect of ranking and social recommendation algorithms on the visibility of minorities. Finally, we highlight the key challenges and future opportunities in this emerging research topic.