真实性与排斥：社交媒体算法与认知共同体中归属感的动态机制
Authenticity and exclusion: social media algorithms and the dynamics of belonging in epistemic communities
AI总结：
本文通过基于智能体的模拟研究社交媒体推荐算法如何系统性损害少数群体研究者的专业可见度、激励同化并降低其内容对多数群体的可见性，揭示技术中介在认知不公正中的作用。
摘要：
近期的哲学研究已经探讨了认知者的社会身份如何影响其贡献被接收、评估和认可的方式。然而，关于技术在当今认知共同体中中介和促成交流方面所扮演的角色，仍存在一个关键空白。本文通过考察社交媒体平台及其推荐算法如何塑造少数群体研究者的专业可见度和机会，来填补这一空白。我们采用基于智能体的模拟方法，针对一个广泛使用的推荐算法的组成部分研究这一问题，并揭示了三个关键模式：第一，这些算法不成比例地损害少数群体研究者的专业可见度，造成系统性的排斥模式。第二，在这些少数群体内部，算法使得那些更接近多数群体特征的用户获得更高的可见度，从而以专业不可见为代价激励同化。第三，即使对于与少数群体身份高度契合的主题，少数群体研究者创作的内容对多数群体而言，其可见度也低于多数群体用户创作的类似内容。重要的是，尽管个体对专业内容的参与行为与群体身份无关，这些模式仍然出现。这些发现对关于认知不公正与排斥的哲学讨论，以及对旨在解决这些伤害的政策提案，都具有重要意义。更广泛地说，它们呼吁更深入地审视人工智能和数据驱动技术在塑造当今认知共同体中所发挥的普遍但常被忽视的作用。
英文摘要：
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.
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