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What should we do with emotion AI? Should we regulate, ban, promote, or re-imagine it? Emotion AI, a class of affective computing technologies used in personal and social computing, comprises emergent and controversial techniques aiming to classify human emotion and other affective phenomena. Industry, policy, and scientific actors debate potential benefits and harms, arguing for polarized futures ranging from panoptic expansion to complete bans. Emotion AI is proposed, deployed, and sometimes withdrawn in collaborative contexts such as education, hiring, healthcare, and service work. Proponents expound these technologies’ benefits for well-being and security, while critics decry privacy harms, civil liberties risks, bias, and shaky scientific foundations, and gaps between technologies’ capabilities and how they are marketed and legitimized. This panel brings diverse disciplinary perspectives into discussion about the history of emotions—as an example of ’intimate’ data—in computing, how emotion AI is legitimized, people’s experiences with and perceptions of emotion AI in social and collaborative settings, emotion AI’s development practices, and using design research to re-imagine emotion AI. These issues are relevant to the CSCW community in designing, evaluating, and regulating algorithmic sensing technologies including and beyond emotion-sensing. 

This paper suggests how reflective design can aid informal participatory algorithm auditing. Drawing from reflective design, we designed a simple web-form probe to invite critical reflection on Emotion AI, ethically controversial techniques predicting individuals’ emotions. Participants engaged the probe throughout their daily lives for about a week. Then, we interviewed participants about their experiences and reflections. Our findings surface themes around participants’ (i) critiques of Emotion AI, (ii) factors contributing to inaccuracy, and (iii) patterns of miscategorization. Our discussion contributes (1) recommendations for Emotion AI and (2) how reflective design may offer considerations to inform algorithm auditing. Overall, our paper suggests ways critically-oriented design research can engage AI ethics through informal, participatory, exploratory algorithm auditing.