Caught between “light skin is beautiful and tanned skin is attractive”: How bicultural socialization shapes attitudes toward skin color aesthetics.

2019 ◽  
Vol 10 (4) ◽  
pp. 326-340 ◽  
Author(s):  
Hsin-Yu Chen ◽  
Nina G. Jablonski ◽  
Garry Chick ◽  
Careen Yarnal
Keyword(s):  
Skin Color ◽  
Color Aesthetics ◽  
Light Skin ◽  
Bicultural Socialization

“Because I’m Light Skin . . . They Think I’m Italian”

2016 ◽  
Vol 52 (4) ◽  
pp. 460-490 ◽  
Author(s):  
Edward Fergus
Keyword(s):  
Skin Color ◽  
Color Variation ◽  
Ethnic Identification ◽  
Opportunity Structure ◽  
Mexican Students ◽  
Light Skin ◽  
Racial Ethnic

Discussions on Latino/a students’ interpretation of the opportunity structure and schooling treat racial/ethnic identification among Latino/as as static, despite skin color variation. This article provides findings from interviews with six Mexican students who discussed teachers identifying them as “White-looking” or “Hispanic/Mexican-looking.” Both groups shared belief in the achievement ideology and understood the opportunity structure as fraught with barriers. However, the “White-looking” students perceived themselves as being able to permeate such barriers meanwhile the “Hispanic/Mexican-looking” students believed such barriers affect their ability to “make it” regardless of their aspirations. This study raises questions regarding theories on academic variability of Latino/a students.

scholarly journals Performance of Artificial Intelligence Imaging Models in Detecting Dermatological Manifestations in Higher Fitzpatrick Skin Color Classifications (Preprint)

2021 ◽  
Author(s):  
Pushkar Aggarwal
Keyword(s):  
Artificial Intelligence ◽  
Basal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Image Recognition ◽  
Basal Cell ◽  
Predictive Value ◽  
Skin Color ◽  
Dermatological Diseases ◽  
Light Skin ◽  
Artificial Intelligence Models

BACKGROUND The performance of deep-learning image recognition models is below par when applied to images with Fitzpatrick classification skin types 4 and 5. OBJECTIVE The objective of this research was to assess whether image recognition models perform differently when differentiating between dermatological diseases in individuals with darker skin color (Fitzpatrick skin types 4 and 5) than when differentiating between the same dermatological diseases in Caucasians (Fitzpatrick skin types 1, 2, and 3) when both models are trained on the same number of images. METHODS Two image recognition models were trained, validated, and tested. The goal of each model was to differentiate between melanoma and basal cell carcinoma. Open-source images of melanoma and basal cell carcinoma were acquired from the Hellenic Dermatological Atlas, the Dermatology Atlas, the Interactive Dermatology Atlas, and DermNet NZ. RESULTS The image recognition models trained and validated on images with light skin color had higher sensitivity, specificity, positive predictive value, negative predictive value, and F1 score than the image recognition models trained and validated on images of skin of color for differentiation between melanoma and basal cell carcinoma. CONCLUSIONS A higher number of images of dermatological diseases in individuals with darker skin color than images of dermatological diseases in individuals with light skin color would need to be gathered for artificial intelligence models to perform equally well.

Relational Racializations

2019 ◽  
pp. 89-101
Author(s):  
Frances R. Aparicio
Keyword(s):  
United States ◽  
Puerto Rico ◽  
Skin Color ◽  
The United States ◽  
Social Identities ◽  
National Identities ◽  
Racial Experiences ◽  
Light Skin ◽  
The Social ◽  
Social Meanings

I examine the racial experiences that four Intralatino/as have had visiting their respective home countries, as well as within their own social circles in Chicago, in being excluded and Othered in terms of their skin color and their multiple, hybrid national identities. These experiences with race and skin color—both dark and light skin colors—are informed by the dominant racial national imaginaries of countries such as Mexico, Puerto Rico, Colombia and Ecuador. While highlighting the relational and situational nature of the social meanings accorded to skin color, these four anecdotes of racial belonging and non-belonging also problematize and complicate our understanding of race and social identities in the United States.

Native American Colorism: From Historical Manifestations to the Current Era

2018 ◽  
Vol 62 (14) ◽  
pp. 2023-2036
Author(s):  
Donna Brown ◽  
Karen Branden ◽  
Ronald E. Hall
Keyword(s):  
African American ◽  
Native American ◽  
Native Americans ◽  
Skin Color ◽  
School Setting ◽  
Dark Skin ◽  
Mixed Blood ◽  
Light Skin ◽  
Victim Group ◽  
Modern Era

Following conquest by European settlers Native Americans internalized Euro-American traditions and ideals. Salient among such ideals was the internalization of a bias as pertains to skin color defined as colorism. Colorism is a quasi-manifestation of racism carried out by victim-group populations. Subsequently, light skin was idealized and dark skin denigrated. Initially the idealization of light skin was dramatically displayed in the school setting. Internal confrontations between Cherokee tribal members were frequent. In the modern era, per confrontations such idealization is exacerbated by the complexity of tribal membership. Said complexity is acted out where those of Euro-American (light-skinned) mixed blood are more favored compared with those of African American (dark-skinned) mixed blood. The accountability of the Euro-American influenced relative to the aforementioned confrontations must be addressed in the quest for resolution.

Preferences for Black Skin among Negro Adults

1973 ◽  
Vol 32 (3_suppl) ◽  
pp. 1171-1175
Author(s):  
Norman H. Hamm ◽  
David O. Williams ◽  
A. Derick Dalhouse
Keyword(s):  
Skin Color ◽  
Age Groups ◽  
Older Age ◽  
Age Group ◽  
Significant Preference ◽  
Dark Skin ◽  
Light Skin ◽  
Black Skin ◽  
Behavioral Attributes

24 black Ss, age 15 to 25, 35 to 45, 55 to 65 yr., were required to choose a real and ideal face from 11 faces which differed in skin color and attribute desirable and undesirable behavioral attributes to 20 figures, 10 of which were Negro. Analyses of the former task showed neither a significant preference on the part of all Ss for dark skin colors nor an increasing tendency for older Ss to prefer light skin; analyses of the latter task also indicated that across all age groups there was no preference for dark skin. However, Ss in the youngest age group attributed significantly more positive behavioral attributes to black skin than Ss in the older age categories.

scholarly journals Automated Bias Reduction in Deep Learning Based Melanoma Diagnosis using a Semi-Supervised Algorithm

2021 ◽  
Author(s):  
Sauman Das
Keyword(s):  
Deep Learning ◽  
Skin Color ◽  
Racial Bias ◽  
Skin Lesions ◽  
Bias Reduction ◽  
Type I ◽  
Melanoma Diagnosis ◽  
Light Skin ◽  
Common Skin ◽  
Significant Performance

AbstractMelanoma is one of the most fatal forms of skin cancer and is often very difficult to differentiate from other benign skin lesions. However, if detected at its early stages, it can almost always be cured. Researchers and data scientists have studied this disease in-depth with the help of large datasets containing high-quality dermascopic images, such as those assembled by the International Skin Imaging Collaboration (ISIC). However, these images often lack diversity and over-represent patients with very common skin features such as light skin and having no visible body hair. In this study, we introduce a novel architecture called LatentNet which automatically detects over-represented features and reduces their weights during training. We tested our model on four distinct categories - three skin color levels corresponding to Type I, II, and III on the Fitzpatrick Scale, and images containing visible hair. We then compared the accuracy against the conventional Deep Convolutional Neural Network (DCNN) model trained using the standard approach (i.e. without detecting over-represented features) and containing the same hyper-parameters as the LatentNet. LatentNet showed significant performance improvement over the standard DCNN model with accuracy of 89.52%, 79.05%, 64.31%, and 64.35% compared to the DCNN accuracy of 90.41%, 70.82%, 45.28%, 56.52% in the corresponding categories, respectively. Differences in the average performance between the models were statistically significant (p < 0.05), suggesting that the proposed model successfully reduced bias amongst the tested categories. LatentNet is the first architecture that addresses racial bias (and other sources of bias) in deep-learning based Melanoma diagnosis.

scholarly journals Bridging The Gap: Demanding 1-1 Representation of Dark-Light Skin Tones Within Medical Lectures/Resources

2021 ◽  
Vol 18 (04) ◽  
Author(s):  
Delice Kayishunge ◽  
Mason Belue
Keyword(s):  
Medical Education ◽  
Medical Students ◽  
Skin Color ◽  
Health Care Disparities ◽  
Top Down ◽  
Dark Skin ◽  
Dark Light ◽  
White Skin ◽  
Accreditation Standard ◽  
Light Skin

Being a good physician means having the ability to recognize diseases in all kinds of individuals. This is especially true for skin lesions (e.g., acne, cancer), which present differently based on skin color and tone. Developing skin-tone-dependent diagnosing skills depends on the medical education (e.g., lectures, medical textbooks, and online board certification prep resources) and hands-on clinical experiences doctors receive. We find it alarming that medical students' gold standard resources overrepresent light skin and underrepresent dark skin to the point where many medical students can recognize a lesion on white skin but fail to recognize a similar lesion on dark skin. This lack of representation perpetuates race as a social determinant of health, leading to missed diagnoses and diagnosis at a later/worse stage in people of color. To combat this underrepresentation within medical education, we propose the Liaison Committee on Medical Education (LCME) amend Accreditation Standard 7: Curricular Content, Subsection 7.6: Cultural Competence and Health Care Disparities. The amendment is to include 1 of the 2 following policy changes, with preference for the top-down mandate: 1) Top-down Mandate: An objective measure and subsequent goal (1:1 representation) for the representation of skin of color within a school's medical lectures, which is evaluated by an LCME-approved curriculum committee and mandated for schools wishing to continue to be LCME accredited. 2) Bottom-up Individualized Institutional Goals: A requirement for schools to choose their own goal, create their committee, and evaluate their progress. These progress reports will be submitted to the LCME annually.

scholarly journals Performance of Artificial Intelligence Imaging Models in Detecting Dermatological Manifestations in Higher Fitzpatrick Skin Color Classifications

10.2196/31697 ◽  
2021 ◽  
Vol 4 (2) ◽  
pp. e31697
Author(s):  
Pushkar Aggarwal
Keyword(s):  
Artificial Intelligence ◽  
Basal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Image Recognition ◽  
Basal Cell ◽  
Predictive Value ◽  
Skin Color ◽  
Dermatological Diseases ◽  
Light Skin ◽  
Artificial Intelligence Models

Background The performance of deep-learning image recognition models is below par when applied to images with Fitzpatrick classification skin types 4 and 5. Objective The objective of this research was to assess whether image recognition models perform differently when differentiating between dermatological diseases in individuals with darker skin color (Fitzpatrick skin types 4 and 5) than when differentiating between the same dermatological diseases in Caucasians (Fitzpatrick skin types 1, 2, and 3) when both models are trained on the same number of images. Methods Two image recognition models were trained, validated, and tested. The goal of each model was to differentiate between melanoma and basal cell carcinoma. Open-source images of melanoma and basal cell carcinoma were acquired from the Hellenic Dermatological Atlas, the Dermatology Atlas, the Interactive Dermatology Atlas, and DermNet NZ. Results The image recognition models trained and validated on images with light skin color had higher sensitivity, specificity, positive predictive value, negative predictive value, and F1 score than the image recognition models trained and validated on images of skin of color for differentiation between melanoma and basal cell carcinoma. Conclusions A higher number of images of dermatological diseases in individuals with darker skin color than images of dermatological diseases in individuals with light skin color would need to be gathered for artificial intelligence models to perform equally well.

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