Glossary // Glossary term Technical
Affective computing
The study and development of computing systems that recognise, interpret, simulate or respond to human affect, a research programme named by Rosalind Picard in 1997. In practice such systems estimate emotional categories or dimensions from signals such as facial movements, voice, text or physiology, and the validity of those estimates depends on the assumed link between signal and state. The field also raises ethical questions about consent, surveillance and the use of such inferences in consequential decisions. Affective computing promises to infer emotion as if it were a system metric. The evidence on facial movements and autonomic signals, together with questions of consent, explains why every metric in this artifact is an artistic value. The works the definition rests on, with the level of evidence each was appraised at. (1997). Affective computing. Supports the historical claim that affective computing was framed as a research field in 1997. It is not evidence that emotion can be validly inferred from signals. (2019). Emotional expressions reconsidered: Challenges to inferring emotion from human facial movements. doi:10.1177/1529100619832930 Supports the claim that facial configurations are not reliable, specific indicators of emotional states across cultures, situations and people, although they co-occur above chance. A narrative review by authors associated with one theoretical position; critics dispute parts of it. (2021). The ethics of emotion in artificial intelligence systems. doi:10.1145/3442188.3445939 Supports the claim that emotion-AI systems depend on contested models of emotion and proxy data, which shapes their ethical implications. A conceptual argument, not an empirical audit of any product.Where the site uses it
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