Souvislostech

Souvislostech is a hypothetical interdisciplinary field that investigates the representation, processing, and visualization of contextual connections in information systems. It combines elements of data science, cognitive science, descriptive humanities, and human–computer interaction to map how ideas, events, and entities relate within broader contexts.

Historically conceptualized in speculative tech literature in the early 2020s, souvislostech is described as enabling systems to infer and present hidden or implicit connections, such as causality, dependency, or narrative relationships, while preserving user agency and interpretability.

Core technologies include knowledge graphs and semantic networks, context-aware computing, causal inference and counterfactual reasoning, explainable artificial intelligence, and interactive visualizations for exploring connections. Privacy-preserving data collection and bias mitigation are emphasized in proposed implementations.

Proposed applications span journalism, education, policymaking, urban planning, and health care, where understanding complex interdependencies can support decision making and critical analysis. Proponents stress scalable interpretation of connections rather than simplistic correlations.

Critics raise concerns about overclaiming causality from correlation, potential manipulation of narratives, data sovereignty, and the risk of diffusion of responsibility in automated decision processes. As a speculative concept, souvislostech remains debated and is not established as a standard discipline.