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Discipline and Bias in the Aesthetics of Face Analysis Algorithms:An Empirical Research on AI Values Based on Computer Vision Technology
CHEN Changfeng, SHI Wen
Chinese Journal of Journalism & Communication ›› 2022, Vol. 44 ›› Issue (3) : 6-33.
PDF(1611 KB)
PDF(1611 KB)
Discipline and Bias in the Aesthetics of Face Analysis Algorithms:An Empirical Research on AI Values Based on Computer Vision Technology
When artificial intelligence is more widely used for assisting humans in making decisions, the value judgment behavior of algorithms deserves the attention of the academic community. This study focused on the esthetic preference of the face analysis algorithms. We chose intelligent face analysis application Face++ and used 1,247 frontal photos of faces taken in a controlled environment to reverse-test the “beauty evaluation” and “face beautify” algorithms provided by Face++. Research found that the algorithm value judgment inherited social value preference and discipline human beings. The study used computer vision technology to calculate the visual age, thinness, and whiteness of faces, and explored their correlation with the automatic “beauty evaluation” score, and examined whether the “face beautify” algorithm results in significant changes to the above attributes. The results confirmed that the “beauty evaluation” algorithm promotes the aesthetics of “thin, white, and young”, and the “face beautify” algorithm further puts this aesthetic preference into an automated aesthetic practice, strengthening the existing preference. Based on the results above, the research further explores the ideological preference behind the aesthetic value of algorithms, and the operational logic of the interaction between algorithms and human aesthetic practices.
AI algorithms / algorithmic ethics / algorithmic values / computer vision / computational communication
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Algorithms are said to affect social realities, often in unseen ways. This article explores conscious, instrumental interactions with algorithms, as a window into the complexities and extent of algorithmic power. Through a thematic analysis of online discussions among Instagram influencers, I observed that influencers’ pursuit of influence resembles a game constructed around “rules” encoded in algorithms. Within the “visibility game,” influencers’ interpretations of Instagram’s algorithmic architecture—and the “game” more broadly—act as a lens through which to view and mechanize the rules of the game. Illustrating this point, this article describes two prominent interpretations, which combine information influencers glean about Instagram’s algorithms with preexisting discourses within influencer communities on authenticity and entrepreneurship. This article shows how directing inquiries toward the visibility game makes present the interdependency between users, algorithms, and platform owners and demonstrates how algorithms structure, but do not unilaterally determine user behavior.
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Many of the images used in traditional forms of mass media have been modified to portray unrealistic and idealised beauty characteristics. Further to this, members of the general public have now begun to digitally enhance their own pictures for social media posts, in order to fulfil these often unattainable standards. Ella Guest explores the impact exposure to idealised images of peers may have on health and wellbeing
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Smart voice assistants have become popular thanks largely to their default naturalistic female voices and helpful personae. In this article, we trace changes in robot voices in popular culture and explain how this history influenced the voice design of smart voice assistants. Our research draws on cultural analysis of Hollywood and international films, television and literature, and observations from our personal experiences with voice assistants. We argue that designers of devices like the Google Home and Amazon Echo inherited a cultural imaginary of alien and dangerous robots with artificial voices and personalities. Manufacturers leveraged techniques of modality, personae and invocation and pre-existing social connotations of the voice to create positive associations of these devices in the home. We conclude by arguing that smart voice assistants are new media innovations prepared for consumers through pre-domestication and represent an emerging regime of power and influence based on technologised voice interaction.
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In this paper, we propose a unified facial beautification framework with respect to skin homogeneity, lighting, and color. A novel region-aware mask is constructed for skin manipulation, which can automatically select the edited regions with great precision. Inspired by the state-of-the-art edit propagation techniques, we present an adaptive edge-preserving energy minimization model with a spatially variant parameter and a high-dimensional guided feature space for mask generation. Using region-aware masks, our method facilitates more flexible and accurate facial skin enhancement while the complex manipulations are simplified considerably. In our beautification framework, a portrait is decomposed into smoothness, lighting, and color layers by an edge-preserving operator. Next, facial landmarks and significant features are extracted as input constraints for mask generation. After three region-aware masks have been obtained, a user can perform facial beautification simply by adjusting the skin parameters. Furthermore, the combinations of parameters can be optimized automatically, depending on the data priors and psychological knowledge. We performed both qualitative and quantitative evaluation for our method using faces with different genders, races, ages, poses, and backgrounds from various databases. The experimental results demonstrate that our technique is superior to previous methods and comparable to commercial systems, for example, PicTreat, Portrait+, and Portraiture.
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Scholars have long been interested in exchange and matching (assortative mating) in romantic partner selection. But many analyses of exchange, particularly those that examine beauty and socioeconomic status, fail to control for partners’ tendency to match each other on these traits. Because desirable traits in mates are positively correlated between partners and within individuals, ignoring matching may exaggerate evidence of cross-trait beauty-status exchange. Moreover, many prior analyses assume a gendered exchange in which women trade beauty for men’s status, without testing whether men might use handsomeness to attract higher-status women. Nor have prior analyses fully investigated how the prevalence of beauty-status exchange varies between different types of couples. I use data from the National Longitudinal Study of Adolescent Health Romantic Pair Sample, a large ( N = 1,507), nationally representative probability sample of dating, cohabiting, and married couples, to investigate how often romantic partners exchange physical attractiveness and socioeconomic status, net of matching on these traits. I find that controlling for matching eliminates nearly all evidence of beauty-status exchange. The discussion focuses on the contexts in which beauty-status exchange is most likely and on implications these results have for market-based and sociobiological theories of partner selection.
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Health systems rely on commercial prediction algorithms to identify and help patients with complex health needs. We show that a widely used algorithm, typical of this industry-wide approach and affecting millions of patients, exhibits significant racial bias: At a given risk score, Black patients are considerably sicker than White patients, as evidenced by signs of uncontrolled illnesses. Remedying this disparity would increase the percentage of Black patients receiving additional help from 17.7 to 46.5%. The bias arises because the algorithm predicts health care costs rather than illness, but unequal access to care means that we spend less money caring for Black patients than for White patients. Thus, despite health care cost appearing to be an effective proxy for health by some measures of predictive accuracy, large racial biases arise. We suggest that the choice of convenient, seemingly effective proxies for ground truth can be an important source of algorithmic bias in many contexts.Copyright © 2019 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.
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Stereotypical thinking and prejudgments from teachers have been found to hinder students' academic and social performance. The relationship between students' race, physical attractiveness, gender, and perceived academic and social competence by both undergraduate education majors and practicing teachers was investigated. Participants were 72 individuals (51 teachers, 21 undergraduate education majors) at a midsized university. Participants viewed eight scenarios (varying by race, gender, and physical attractiveness of target child) in which a child's picture was displayed. Participants then rated the child's social and academic competence on an eight-question, 5-point, Likert-type scale. A 4-way (Child Gender × Child Race × Child Physical Attractiveness × Rater Educational Level) mixed factorial design was used to test the hypotheses. Results showed that the lowest competency ratings were for Black, unattractive boys. No differences were found for ratings by teacher education level. These findings have implications for training of classroom teachers.
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Previous research on the success of politicians’ messages on social media has so far focused on a limited number of platforms, especially Facebook and Twitter, and predominately studied the effects of textual content. This research reported here applies computer vision analysis to a total of 59,020 image posts published by 172 Instagram accounts of U.S. politicians, both candidates and office holders, and examines how visual attributes influence audience engagement such as likes and comments. In particular, this study introduces an unsupervised approach that combines transfer learning and clustering techniques to discover hidden categories from large-scale visual data. The results reveal that different self-personalization strategies in visual media, for example, images featuring politicians in private, nonpolitical settings, showing faces, and displaying emotions, generally increase audience engagement. Yet, a significant portion of politician’s Instagram posts still fell into the traditional, “politics-as-usual” type of political communication, showing professional settings and activities. The analysis explains how self-personalization is embodied in specific visual portrayals and how different self-presentation strategies affect audience engagement on a popular but less studied social media platform.
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Fitspiration is a relatively new social media trend nominally intended to promote health and fitness. Fitspiration messages are presented as encouraging; however, they might also engender body dissatisfaction and compulsive exercise. This study analyzed fitspiration content (n = 1050) on the image-based social media platform Pinterest. Independent raters coded the images and text present in the posts. Messages were categorized as appearance- or health-related, and coded for Social Cognitive Theory constructs: standards, behaviors, and outcome expectancies. Messages encouraged appearance-related body image standards and weight management behaviors more frequently than health-related standards and behaviors, and emphasized attractiveness as motivation to partake in such behaviors. Results also indicated that fitspiration messages include a comparable amount of fit praise (i.e., emphasis on toned/defined muscles) and thin praise (i.e., emphasis on slenderness), suggesting that women are not only supposed to be thin but also fit. Considering the negative outcomes associated with both exposure to idealized body images and exercising for appearance reasons, findings suggest that fitspiration messages are problematic, especially for viewers with high risk of eating disorders and related issues.
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Our study investigated factors that influence attitudes toward cosmetic surgery in middle-aged women. A sample of 108 women, aged between 35 and 55 years, completed questionnaire measures of body dissatisfaction, appearance investment, aging anxiety, media exposure (television and magazine), and attitudes toward cosmetic surgery (delineated in terms of general attitudes, social motivations, and actual consideration). Body dissatisfaction, appearance investment, aging anxiety, and both media variables predicted some facet of attitudes toward cosmetic surgery. Specifically, appearance investment, aging anxiety, and television exposure were unique predictors of endorsement of social motivations for cosmetic surgery, whereas body dissatisfaction, appearance investment, and television exposure were unique predictors of actual consideration of cosmetic surgery. Regression analysis revealed that the effects of media on cosmetic surgery attitudes were primarily direct. We concluded that there are multiple influences on attitudes toward cosmetic surgery for women of middle age.
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'Fitspiration' is an online trend designed to inspire viewers towards a healthier lifestyle by promoting exercise and healthy food. This study provides a content analysis of fitspiration imagery on the social networking site Instagram. A set of 600 images were coded for body type, activity, objectification and textual elements. Results showed that the majority of images of women contained only one body type: thin and toned. In addition, most images contained objectifying elements. Accordingly, while fitspiration images may be inspirational for viewers, they also contain a number of elements likely to have negative effects on the viewer's body image.
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As algorithms come to govern every aspect of our lives—from bank loans, to job applications, to traffic patterns, to our media consumption patterns—communication research has become increasingly concerned with how we govern algorithms. Building on the methodological frameworks established by critical information researchers like Safiya Noble, Tarleton Gillespie, and Nick Seaver, this essay argues that we do not need to reverse engineer the “black box” to understand its impacts because they can be found through qualitative methodologies instead. This essay rejects the “black box” as an epistemic premise upon which critical algorithmic literacies can be built by using discourse analysis to observe how the unknowable language of the algorithm is deployed discursively within gamer communities to establish and maintain patriarchal power. This essay shows how the “black box” is used by fan communities to advance a patriarchal understanding of what we term paradigms of “balance” and “realism” in game design.
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A few cross-sectional studies have found that selfie-related behaviors have positive associations with self-objectification or appearance concerns, but little is known about whether bidirectional relationships exist between selfie behaviors and these body-related variables over time. The present study examined the reciprocal relationships between selfie-related behaviors and both self-objectification and appearance concerns among adolescents using a longitudinal design. Chinese adolescent boys and girls completed questionnaires at baseline and at 6-month follow-up. The key constructs included selfie-posting, selfie-editing, selfie-viewing, self-objectification, facial dissatisfaction, and body dissatisfaction. Results indicated that selfie-editing, but not selfie-posting, predicted increases in adolescents’ self-objectification and appearance concerns (both body and face) over time. Selfie-viewing predicted increases in self-objectification and facial dissatisfaction, but not body dissatisfaction over time. In the other direction, adolescents’ antecedent levels of self-objectification predicted increases in subsequent selfie-related behaviors. In addition, adolescents’ facial dissatisfaction positively predicted selfie-viewing and selfie-editing but not selfie-posting over time, whereas body dissatisfaction had no influence on subsequent selfie-related behaviors among adolescents. Findings from this study provide new insights into the reciprocal relationships between selfie-related behaviors and body image.
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Organizations often employ data-driven models to inform decisions that can have a significant impact on people's lives (e.g., university admissions, hiring). In order to protect people's privacy and prevent discrimination, these decision-makers may choose to delete or avoid collecting social category data, like sex and race. In this article, we argue that such censoring can exacerbate discrimination by making biases more difficult to detect. We begin by detailing how computerized decisions can lead to biases in the absence of social category data and in some contexts, may even sustain biases that arise by random chance. We then show how proactively using social category data can help illuminate and combat discriminatory practices, using cases from education and employment that lead to strategies for detecting and preventing discrimination. We conclude that discrimination can occur in any sociotechnical system in which someone decides to use an algorithmic process to inform decision-making, and we offer a set of broader implications for researchers and policymakers.
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Human physical characteristics and their perception by the brain are under pressure by natural selection to optimize reproductive success. Men and women have different strategies to appear attractive and have different interests in identifying beauty in people. Nevertheless, men and women from all cultures agree on who is and who is not attractive, and throughout the world attractive people show greater acquisition of resources and greater reproductive success than others. The brain employs at least three modules, composed of interconnected brain regions, to judge facial attractiveness: one for identification, one for interpretation and one for valuing. Key elements that go into the judgment are age and health, as well as symmetry, averageness, face and body proportions, facial color and texture. These elements are all Costly Signals of reproductive fitness because they are difficult to fake. However, people deceive others using tricks such as coloring hair, cosmetics and clothing styles, while at the same time they also focus on detecting fakes. People may also deceive themselves, especially about their own attractiveness, and use self-signally actions to demonstrate to themselves their own true value. The neuroscience of beauty is best understood by considering the evolutionary pressures to maximize reproductive fitness.
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Introduction: With the rise of camera phones, selfie-taking has become a normative part of our modern culture. However, little is known about how this behavior may relate to eating disorder (ED) characteristics, particularly in those who already have eating disorder symptoms of clinical severity. The current study investigated how selfie-posting and selfie-taking with no intention of posting online (offline selfies) were related to ED symptoms. Method: A total of 152 females (average age 22.44 years) with ED symptoms of clinical severity completed self-report questionnaires measuring selfie-frequency (online and offline), frequency of non-selfie photo posting, social networking site use, body dissatisfaction, body checking, ED symptom severity, self-esteem and body avoidance. Responses were collected via an ED social community. Results: No direct relationship, or indirect association via body dissatisfaction, was found between selfie behavior and ED symptom severity. However, the more offline selfies an individual took, the more frequently they body checked, and this, in turn, was related to greater ED symptom severity. Conclusions: These results suggest that offline selfies may be a modern form of body checking. Our findings are the first to imply that offline selfie-taking may be a problematic behavior and a potential maintenance factor for individuals with severe ED symptoms.
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1. 库布里克式的科幻构想：库布里克于1968年拍摄科幻电影《2001太空漫游》，电影中有一台名为“HAL9000”的智能电脑，该电脑试图进行自主决策并导致四名人类丧生。
2. “电车难题 ”：一项伦理学领域的著名思想实验，来自于1967年菲利帕·福特发表的《堕胎问题和教条双重影响》。其问题是：当一辆失控的电车即将冲向前方轨道上的五个人时，有一个拉杆可以使电车开上另外一个轨道，该轨道上有一个人，是否应该拉动拉杆?
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