This conceptual contribution offers a speculative account of how AI systems might emulate emotions as experienced by humans and animals. It presents a thought experiment grounded in the hypothesis that natural emotions evolved as heuristics for rapid situational appraisal and action selection, enabling biologically adaptive behaviour without requiring full deliberative modeling. The text examines whether artificial systems operating in complex action spaces could similarly benefit from these principles. It is proposed that affect be interwoven with episodic memory by storing corresponding affective tags alongside all events. This allows AIs to establish whether present situations resemble past events and project the associated emotional labels onto the current context. These emotional cues are then combined with need-driven emotional hints. The combined emotional state facilitates decision-making in the present by modulating action selection. The low complexity and experiential inertness of the proposed architecture are emphasized as evidence that emotional expression and consciousness are, in principle, orthogonal-permitting the theoretical possibility of affective zombies. On this basis, the moral status of AIs emulating affective states is critically examined. It is argued that neither the mere presence of internal representations of emotion nor consciousness alone suffices for moral standing; rather, the capacity for self-awareness of inner emotional states is posited as a necessary condition. A complexity-based criterion is proposed to exclude such awareness in the presented model. Additional thought experiments are presented to test the conceptual boundaries of this framework.

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Artificial Intelligence (AI) systems that companies claim can "read" facial expressions is based on outdated science and risks being unreliable and discriminatory, one of the world's leading experts on the psychology of emotion has warned. Lisa Feldman Barrett, professor of psychology at Northeastern University, said that such technologies appear to disregard a growing body of evidence undermining the notion that the basic facial expressions are universal across cultures. As a result, such technologies – some of which are already being deployed in real-world settings – run the risk of being unreliable or discriminatory, she said. "I don't know how companies can continue to justify what they're doing when it's really clear what the evidence is," she said. "There are some companies that just continue to claim things that can't possibly be true."