Social Context in Human-AI Interaction (HAI): Towards a Theoretical Framework Based on Multi-Perspectival Imaginaries

Despite decades of awareness, asymmetries in conceptual resources, data, and governance between users and developers persist. Meanwhile, socially situated ‘end-users’ experience harms, such as hidden labor, reinforced stereotypes, and increased unpredictability, as machine learning advances. However, accurately modeling social context does not constitute design justice. This paper reinterprets Giuseppe Mantovani’s model of social context in human-computer interaction (HCI) to address the relational emergence and political situatedness of social context in human-AI interaction (HAI). Drawing on the concept of a multi-perspectival algorithmic imaginary, we (1) conceptualize context as nested and entangled relations, (2) identify actors, including users, developers, models, and interfaces, and (3) foreground the organization of relations of power. Recognizing that models are performative rather than predictive, we argue that the sociopolitical situatedness of context must be acknowledged alongside its limits. To challenge design authority, we propose that HCI and HAI strategically foster intra-actional negotiability, enabling users to challenge, reinterpret, or modify their relationship with technology through critical (design) practices.