Authenticity and exclusion: social media algorithms and the dynamics of belonging in epistemic communities

TL;DR

This paper examines how social media platforms and their recommendation algorithms shape the professional visibility and opportunities of researchers from minority groups, and calls for a closer examination of the pervasive, but often neglected role of AI and data-driven technologies in shaping today's epistemic communities.

Abstract

Recent philosophical work has explored how the social identity of knowers influences how their contributions are received, assessed, and credited. However, a critical gap remains regarding the role of technology in mediating and enabling communication within today's epistemic communities. This paper addresses this gap by examining how social media platforms and their recommendation algorithms shape the professional visibility and opportunities of researchers from minority groups. Using agent-based simulations, we investigate this question with respect to components of a widely used recommendation algorithm, and uncover three key patterns: First, these algorithms disproportionately harm the professional visibility of researchers from minority groups, creating systemic patterns of exclusion. Second, within these minority groups, the algorithms result in greater visibility for users who more closely resemble the majority group, incentivizing assimilation at the cost of professional invisibility. Third, even for topics that strongly align with minority identities, content created by minority researchers is less visible to the majority than similar content produced by majority users. Importantly, these patterns emerge, even though individual engagement with professional content is independent of group identity. These findings have significant implications for philosophical discussions on epistemic injustice and exclusion, and for policy proposals aimed at addressing these harms. More broadly, they call for a closer examination of the pervasive, but often neglected role of AI and data-driven technologies in shaping today's epistemic communities.

Figures (3)

• Figure 1: Recommendation results for professional topic content using real graph recommendation policy with colors indicating different network structures. Results are averaged over 20 simulation runs.
• Figure 2: Average number of times professional content created by the minority group is recommended to majority group members in relationship to the number of incoming and outgoing edges and topic preferences. Each point corresponds to one minority group user in one 20 simulation runs using a homophilic network structure and real graph recommendation policy. Lines indicate linear approximations.
• Figure 3: Average share of minority group recommendations compared across content topics for minority, majority, and all groups. Data covers 20 simulation runs with 10,000 time steps using real graph recommendation policy and homophilic network structure. Red lines depict minority group share in content creation for each group, white dots represent means.