The data were collected during citizen workshops, conducted online via Zoom, exploring attitudes towards emotional artificial intelligence use (EAI). EAI is the use of affective computing and AI techniques to try to sense and interact with human emotional life, ranging from monitoring emotions through biometric data to more active interventions. 10 sets of participants (n=46) were recruited for the following groups: 3 older (65+) groups: n=13 3 younger (18-34) groups: n=12 2 groups, people self-identifying as disabled: n=10 2 groups, members of UK ethnic minorities: n=11 There was an attempt to balance other demographic categories where possible. Participants were grouped in relation to age as this has been shown to be the biggest indicator of differences in attitude towards emotional AI (Bakir & McStay, 2020; McStay, 2020). It was also considered important to include the views of those who have traditionally been ignored in the development of technology or suffered further discrimination through its use, and so the opinions and perspectives of minority groups and disabled people were sought. Participants were recruited through a research panel for the workshops, which took place in August 2021. A novel narrative approach was used, with participants taken through a piece of interactive fiction (developed using Twine, viewable here: https://eaitwine.neocities.org/), a day-in-the life story of a protagonist encountering seven mundane use-cases of emotional AI, each structured as a) a neutral introduction to the technology; b) a binary choice involving the use of the technology; c) a ContraVision component demonstrating positive and negative events/outcomes. The use cases were: • Home-hub smart assistant • Bus station surveillance sensor • Social Media Fake news/Disinformation and profiling. • Spotify music recommendations (using voice and ambient data). • Sales call evaluation and prompt tool • Emotoy that collects and responds to children's emotional data. • Hire car in-cabin customisation and driving support. Each workshop lasted 2 hours. Audio files were transcribed using a transcription service before being corrected and formatted by a project researcher. References: Bakir, V., & McStay, A. (2020). Profiling & Targeting Emotions in Digital Political Campaigns. Briefing Paper for All Party Parliamentary Group on Electoral Campaigning Transparency. McStay, A. (2020). Emotional AI, soft biometrics and the surveillance of emotional life: An unusual consensus on privacy. Big Data & Society, 7(1), 1–12. https://doi.org/10.1177/2053951720904386