Digital technologies and AI promise to optimise complex systems through data-driven decisions, predictive modelling, and anticipatory action. However, this optimisation imperative creates a fundamental paradox: as systems excel at achieving measurable objectives, they may erode the collective intelligence and adaptive capacity of our societies. Recognising this tension, the field of Human-Centred AI (HCAI) has emerged to develop design principles such as explainability, fairness, and transparency to ensure that AI aligns with human values. However, research on HCAI often focuses on idealised interactions, neglecting the pressure, moral dilemmas, and social dynamics typical of today’s complex problems. This paper introduces and advocates for a paradigm shift towards Sensemaking AI: AI that supports collective meaning-making processes in evolving human-AI networks. This novel perspective recognises that algorithmic and AI systems actively participate in the social processes through which humans interpret information, coordinate responses, and adapt their values. Grounded in sensemaking and decision theory and informed by a scoping review of the HCAI literature, this paper identifies three connected research areas: (i) sensemaking-aware automation that preserves interpretive flexibility; (ii) collective agency for network-level control; and (iii) value-aware sensemaking that supports collective meaning-making. These principles form the basis for Sensemaking AI as a design and research agenda that prioritises collective meaning-making and democratic deliberation in networks.
Tina Comes (Thu,) studied this question.