Contemporary automobiles are now incorporating digital technologies, including emotion recognition technologies intended to monitor and sometimes intervene on the driver’s mood, attentiveness, or emotional state. We investigate how the firms producing these technologies justify and legitimate their design, production, and use, and how these discourses of legitimation paint a picture of the desired social role of emotion recognition in the automotive sector. Through a critical discourse analysis of patents, advertising, and promotional materials from industry-leading companies Cerence and Affectiva/Smart Eye, we argue both companies use potentially spurious arguments about the accuracy of emotion recognition to rationalize their products. Both companies also use a variety of other legitimation techniques around driver safety, individual personalization, and increased productivity to re-frame the social aspects of digitally mediated autonomous vehicles on their terms.

Algorithms and other formal models purportedly incorporating human values like fairness have grown increasingly popular in computer science. In response to sociotechnical challenges in the use of these models, designers and researchers have taken widely divergent positions on how formal models incorporating aspects of human values should be used: encouraging their use, moving away from them, or ignoring the normative consequences altogether. In this paper, we seek to resolve these divergent positions by identifying the main conceptual limits of formal modeling, and develop four reflexive values--value fidelity, appropriate accuracy, value legibility, and value contestation--vital for incorporating human values adequately into formal models. We then provide a brief methodology for reflexively designing formal models incorporating human values.

Many organizations have published principles intended to guide the ethical development and deployment of AI systems; however, their abstract nature makes them difficult to operationalize. Some organizations have therefore produced AI ethics checklists, as well as checklists for more specific concepts, such as fairness, as applied to AI systems. But unless checklists are grounded in practitioners' needs, they may be misused. To understand the role of checklists in AI ethics, we conducted an iterative co-design process with 48 practitioners, focusing on fairness. We co-designed an AI fairness checklist and identified desiderata and concerns for AI fairness checklists in general. We found that AI fairness checklists could provide organizational infrastructure for formalizing ad-hoc processes and empowering individual advocates. We highlight aspects of organizational culture that may impact the efficacy of AI fairness checklists, and suggest future design directions.

Abstract Employers are increasingly using information and communication technologies to monitor employees. Such workplace surveillance is extensive in the United States, but its experience and potential consequences differ across groups based on gender. We thus sought to identify whether self‐reported male and female employees differ in the extent to which they find the use of workplace cameras equipped with facial recognition technology (FRT) acceptable, and examine the role of privacy attitudes more generally in mediating views on workplace surveillance. Using data from a nationally representative survey conducted by the Pew Research Center, we find that women are much less likely than men to approve of the use of cameras using FRT in the workplace. We then further explore whether men and women think differently about privacy, and if perceptions of privacy moderate the relationship between gender and approval of workplace surveillance. Finally, we consider the implications of these findings for privacy and surveillance via embedded technologies, and how the consequences of surveillance and technologies like FRT may be gendered. Note: We recognize evaluations based on a binary definition of gender are invariably partial and exclusionary. As we note in our discussion of the study's limitations, we were constrained by the survey categories provided by Pew.

We are surrounded by digital images of personal lives posted online. Changes in information and communications technology have enabled widespread sharing of personal photos, increasing access to aspects of private life previously less observable. Most studies of privacy online explore differences in individual privacy preferences. Here we examine privacy perceptions of online photos considering both social norms, collectively—shared expectations of privacy and individual preferences. We conducted an online factorial vignette study on Amazon’s Mechanical Turk ( n = 279). Our findings show that people share common expectations about the privacy of online images, and these privacy norms are socially contingent and multidimensional. Use of digital technologies to share personal photos is influenced by social context as well as individual preferences, while such sharing can affect the social meaning of privacy.