Morphological gender recognition by a social robot and privacy concerns

Ramey, Arnaud; Salichs, Miguel Á.

doi:10.1145/2559636.2563714

Cited by 9 publications

(6 citation statements)

References 6 publications

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“…This binary categorization is often extended to -and thus reinforced by -virtual settings, e.g., games, social media platforms, web forms, etc., where a binary male/female indicator is presented to the user to indicate their gender [138]. Another example of gendered design is an algorithm that scans the size of breasts to detect the gender of a person [167]. In addition, researchers have pointed out that binary design in automatic gender recognition are trans-gender-exclusionary [115].…”

Section: Stereotype 2: the Gender Binarymentioning

confidence: 99%

Gender-Inclusive HCI Research and Design: A Conceptual Review

Stumpf¹,

Peters²,

Bardzell³

et al. 2020

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Previous research has investigated gender and its implications for HCI. We consider inclusive design of technology whatever the gender of its users of particular importance. This conceptual review provides an overview of the motivations that have driven research in gender and inclusive HCI design. We review the empirical evidence for the impact of gender in thinking and behavior which underlies HCI research and design. We then present how HCI design might inadvertently embed and perpetuate gender stereotypes. We then present current HCI design approaches to tackle gender stereotypes and to produce gender-inclusive designs. We conclude by discussing possible future directions in this area.

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Section: Stereotype 2: the Gender Binarymentioning

confidence: 99%

Gender-Inclusive HCI Research and Design: A Conceptual Review

Stumpf¹,

Peters²,

Bardzell³

et al. 2020

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“…Automated facial analysis, and its associated gender classification techniques, have been proposed for a variety of applications, such as access control, real-time security, targeted marketing, and personalized human-robot interaction (e.g. [80,81,90,100]).…”

Section: 3mentioning

confidence: 99%

“…and orientation of the face within the image (i.e., roll, yaw, and pitch). While facial features may be indicative of gender (e.g., facial morphology [68,90]), in this analysis, we focus on the classification data that would most commonly be used by third-party developers making use of these services.…”

Section: The Schema Of a "Face"mentioning

confidence: 99%

“…Literature on gender classification has highlighted numerous methods for identifying gender (e.g. periocular regions [68]; facial morphology [90]; lips, eyes, and cheeks [76]), but how, when, and by whom gender is embedded into the pipeline of data, labels, and models is opaque to outsiders (e.g. [56,89]).…”

Section: Classifying Human Gender Performativity Through Infrastructurementioning

confidence: 99%

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How Computers See Gender

Scheuerman

Paul

Brubaker

2019

Proc. ACM Hum.-Comput. Interact.

151

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Investigations of facial analysis (FA) technologies-such as facial detection and facial recognition-have been central to discussions about Artificial Intelligence's (AI) impact on human beings. Research on automatic gender recognition, the classification of gender by FA technologies, has raised potential concerns around issues of racial and gender bias. In this study, we augment past work with empirical data by conducting a systematic analysis of how gender classification and gender labeling in computer vision services operate when faced with gender diversity. We sought to understand how gender is concretely conceptualized and encoded into commercial facial analysis and image labeling technologies available today. We then conducted a two-phase study: (1) a system analysis of ten commercial FA and image labeling services and (2) an evaluation of five services using a custom dataset of diverse genders using self-labeled Instagram images. Our analysis highlights how gender is codified into both classifiers and data standards. We found that FA services performed consistently worse on transgender individuals and were universally unable to classify non-binary genders. In contrast, image labeling often presented multiple gendered concepts. We also found that user perceptions about gender performance and identity contradict the way gender performance is encoded into the computer vision infrastructure. We discuss our findings from three perspectives of gender identity (self-identity, gender performativity, and demographic identity) and how these perspectives interact across three layers: the classification infrastructure, the third-party applications that make use of that infrastructure, and the individuals who interact with that software. We employ Bowker and Star's concepts of "torque" and "residuality" to further discuss the social implications of gender classification. We conclude by outlining opportunities for creating more inclusive classification infrastructures and datasets, as well as with implications for policy.CCS Concepts: • Social and professional topics → User characteristics; Gender.

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“…6 Scholars in the humanrobot interaction (HRI) community emphasize privacy, among other values, as an important benchmark in creating humanlike robots (Kahn, Ishiguro, Friedman, & Kanda, 2006). Other HRI research tacitly acknowledges the ways technical design and algorithms may encode values, such as the privacy implications of using robots to conduct gender recognition (Ramey & Salichs, 2014) or studying video manipulation techniques to obscure images and preserve privacy in robotic systems (Hubers et al, 2015). Technology is already in use to address privacy and other issues related to drone use.…”

Section: The Drones and Privacy Discoursementioning

confidence: 99%

These Aren’t the Autonomous Drones You’re Looking for: Investigating Privacy Concerns Through Concept Videos

Wong

Mulligan

2016

Journal of Human-Robot Interaction

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Regulators and privacy advocates increasingly demand that privacy be protected through the technical design of products and services, as well as through organizational procedures and policies. Privacy research by computer scientists and engineers are producing insights and techniques that empower a new professional in the technology sector-the privacy engineer. Despite great enthusiasm for this approach, there has been little effort to explore if and how this new direction in privacy protection is influencing the design of products. Understanding how design is being used to protect privacy requires analysis of sociotechnical systems, not decontextualized technical artifacts. We analyze how privacy concerns in public policy debates about drones are raised and addressed in two concept videos from 2013 and 2015 developed by Amazon that depict fictional scenarios involving its future automated drone package delivery service. Drawing on design and communications methods we find that the concept videos reveal increased attention over time to privacy concerns. Our findings offer some evidence that privacy concerns are influencing Amazon's product and service design. Representations about the service offered in the 2015 video shape consumer expectations about how it addresses privacy concerns. While the videos reviewed do not represent an existing product, we discuss the shifting role such concept videos might play when Amazon's drone delivery service comes to market. As consumer facing representations of product functionality, concept videos, like other public statements, if misleading could form the basis of a deceptive statements claim by the Federal Trade Commission or state consumer protection agency. Finally, reflecting on our review, we suggest that concept videos are a useful tool for engaging regulators and other stakeholders in contextually specific considerations of when and how to enlist product and system design to protect privacy.

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Morphological gender recognition by a social robot and privacy concerns

Cited by 9 publications

References 6 publications

Gender-Inclusive HCI Research and Design: A Conceptual Review

Gender-Inclusive HCI Research and Design: A Conceptual Review

How Computers See Gender

These Aren’t the Autonomous Drones You’re Looking for: Investigating Privacy Concerns Through Concept Videos

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