Modeling understanding through synthetic empathy and the sociotechnical reconfiguration of care

This article examines synthetic empathy as a sociotechnical model of understanding in digitally mediated environments. Rather than asking whether artificial intelligence can genuinely feel or possess inner emotional states, the study analyzes how empathy is translated into computational categories, institutionalized within algorithmic infrastructures, and normalized through everyday interaction. Drawing on the sociology of technology, communication theory, and interdisciplinary scholarship on affect, the article develops a theoretical framework that reconceptualizes empathy as a structured and operational construct embedded in data-driven systems. The analysis argues that synthetic empathy represents a shift from relational interpretation toward predictive modeling, in which emotional responsiveness is formalized through classification, probabilistic inference, and calibrated output. This transformation contributes to a broader reconfiguration of care and understanding, aligning them with institutional priorities such as scalability, consistency, and optimization. In this process, complex emotional experiences are frequently translated into simplified computational categories that enable scalable interaction across digital platforms. As algorithmic mediation becomes embedded in social life, the criteria for attributing understanding increasingly reflect performance-oriented benchmarks shaped by platform logics. By situating synthetic empathy within sociotechnical processes rather than ontological debates about machine consciousness, the article offers a conceptual contribution to contemporary discussions on digital mediation, emotional labor, and the social construction of intelligence. It demonstrates that artificial intelligence does not simply simulate empathy but participates in redefining the cultural grammar through which empathy and understanding are recognized in digital societies.