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Patent applications provide insight into how inventors imagine and legitimize uses of their imagined technologies; as part of this imagining they envision social worlds and produce sociotechnical imaginaries. Examining sociotechnical imaginaries is important for emerging technologies in high-stakes contexts such as the case of emotion AI to address mental health care. We analyzed emotion AI patent applications (N=58) filed in the U.S. concerned with monitoring and detecting emotions and/or mental health. We examined the described technologies' imagined uses and the problems they were positioned to address. We found that inventors justified emotion AI inventions as solutions to issues surrounding data accuracy, care provision and experience, patient-provider communication, emotion regulation, and preventing harms attributed to mental health causes. We then applied an ethical speculation lens to anticipate the potential implications of the promissory emotion AI-enabled futures described in patent applications. We argue that such a future is one filled with mental health conditions' (or 'non-expected' emotions') stigmatization, equating mental health with propensity for crime, and lack of data subjects' agency. By framing individuals with mental health conditions as unpredictable and not capable of exercising their own agency, emotion AI mental health patent applications propose solutions that intervene in this imagined future: intensive surveillance, an emphasis on individual responsibility over structural barriers, and decontextualized behavioral change interventions. Using ethical speculation, we articulate the consequences of these discourses, raising questions about the role of emotion AI as positive, inherent, or inevitable in health and care-related contexts. We discuss our findings' implications for patent review processes, and advocate for policy makers, researchers and technologists to refer to patent (applications) to access, evaluate and (re)consider potentially harmful sociotechnical imaginaries before they become our reality.
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Automated Emotion Recognition in the Workplace: How Proposed Technologies Reveal Potential Futures of Work
Emotion recognition technologies, while critiqued for bias, validity, and privacy invasion, continue to be developed and applied in a range of domains including in high-stakes settings like the workplace. We set out to examine emotion recognition technologies proposed for use in the workplace, describing the input data and training, outputs, and actions that these systems take or prompt. We use these design features to reflect on these technologies' implications using the ethical speculation lens. We analyzed patent applications that developed emotion recognition technologies to be used in the workplace (N=86). We found that these technologies scope data collection broadly; claim to reveal not only targets' emotional expressions, but also their internal states; and take or prompt a wide range of actions, many of which impact workers' employment and livelihoods. Technologies described in patent applications frequently violated existing guidelines for ethical automated emotion recognition technology. We demonstrate the utility of using patent applications for ethical speculation. In doing so, we suggest that 1) increasing the visibility of claimed emotional states has the potential to create additional emotional labor for workers (a burden that is disproportionately distributed to low-power and marginalized workers) and contribute to a larger pattern of blurring boundaries between expectations of the workplace and a worker's autonomy, and more broadly to the data colonialism regime; 2) Emotion recognition technology's failures can be invisible, may inappropriately influence high-stakes workplace decisions and can exacerbate inequity. We discuss the implications of making emotions and emotional data visible in the workplace and submit for consideration implications for designers of emotion recognition, employers who use them, and policymakers.
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- Award ID(s):
- 2020872
- PAR ID:
- 10437781
- Date Published:
- Journal Name:
- Proceedings of the ACM on Human-Computer Interaction
- Volume:
- 7
- Issue:
- CSCW1
- ISSN:
- 2573-0142
- Page Range / eLocation ID:
- 1 to 37
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1145/3579528
