Human–algorithm interactions in platform governance: how Chinese moderators balance conflicting logics in algorithm-assisted decision-making

Abstract Content moderation entails human–algorithm collaborative decision-making in digital platforms subject to multiple socio-structural forces. This article applies perspectives of technology affordances and institutional logics to unpack content moderation as human–algorithm interaction processes grounded in the dynamic network of platform governance. We collaborated with three “super platforms” in China delivering wide-ranging services for research and entered these platforms for fieldwork. Based on participant observations in these platforms and interviews with their staff members, we analyze how the state logic prioritizing socio-political security and the corporation logic stressing commercial interest are embedded in the moderation workflow and distill three affordances of algorithmic decision-making that affect how moderators balance these logics. We find that these affordances play a significant role in helping human moderators balance competing logics: They nudge moderators with divergent preferences into developing comprehensive understandings of governance logics for organizational coherence within the platform company and help moderators strategically integrate the conflicting logics in an iterative process for more accurate decision-making. Moving beyond the human/algorithm divide in existing research stressing the differences and contradictions between algorithmic decision-making and human judgements, our study sheds light on the positive potentials of human–algorithm interactions in addressing shifting requirements of platform governance.