Malaika Handa, Amber Thomas, and Jan Diehn created a beautiful, interactive website, Colorism in High Fashion. It used machine learning to investigate "colorism" at Vogue magazine. Specifically, it delves into the differences, over time, in cover model color but also how lighting and photoshopping can change the color of the same woman's skin, depending on the photo.
There are soooo many ways to use this in class, ranging from machine learning, how machine learning can refine old psychology methodology, to variability and within/between-group differences. Read on:
1. I'm a social psychologist. Most of us who teach social psychology have encountered research that uses magazine cover models as a proxy for what our culture emphasizes and values (1, 2, 3). Here, Malaika Handa, Amber Thomas, and Jan Diehn apply this methodology to Vogue magazine covers. And they take this methodology into the age of machine learning by using k-means cluster and pixels to determine the skin color of models using.
The screenshots below demonstrate the process in a way even a noob can understand (for more on clustering, I recommend this piece by Chelsea Parlett-Pelleriti)
2. K-means clustering, featuring an easy to follow example couched in psychology-relevant research
3. Variability. Skin tone becomes more varied over the passage of time. NOTE: I could only view this portion of the website via Internet Explorer (?!).
4. False positives and double-checking your data! Handa trained her software to find faces. There were false positives, as she illustrated on Twitter.
AND she addressed this in the Methods section.
5. Good science communication. Even if you don't understand statistics or machine learning, you can follow this article. When it comes to science and data communication, this isn't always the case.
6. As technology changes, so does research methodology. In order to use magazine cover models as data, researchers (let's face it, RAs) used to individually rate and code magazine cover models. Now, we can teach computers to do so.
7. Within and between group differences:
Color changes over time (between groups, by year):
Color changes just for one woman, based on lighing, airbrushing, etc. (within-group, by woman. Or, by queen, as in this example featuring Rhianna):
There are soooo many ways to use this in class, ranging from machine learning, how machine learning can refine old psychology methodology, to variability and within/between-group differences. Read on:
1. I'm a social psychologist. Most of us who teach social psychology have encountered research that uses magazine cover models as a proxy for what our culture emphasizes and values (1, 2, 3). Here, Malaika Handa, Amber Thomas, and Jan Diehn apply this methodology to Vogue magazine covers. And they take this methodology into the age of machine learning by using k-means cluster and pixels to determine the skin color of models using.
The screenshots below demonstrate the process in a way even a noob can understand (for more on clustering, I recommend this piece by Chelsea Parlett-Pelleriti)
2. K-means clustering, featuring an easy to follow example couched in psychology-relevant research
3. Variability. Skin tone becomes more varied over the passage of time. NOTE: I could only view this portion of the website via Internet Explorer (?!).
4. False positives and double-checking your data! Handa trained her software to find faces. There were false positives, as she illustrated on Twitter.
5. Good science communication. Even if you don't understand statistics or machine learning, you can follow this article. When it comes to science and data communication, this isn't always the case.
6. As technology changes, so does research methodology. In order to use magazine cover models as data, researchers (let's face it, RAs) used to individually rate and code magazine cover models. Now, we can teach computers to do so.
7. Within and between group differences:
Color changes over time (between groups, by year):
Color changes just for one woman, based on lighing, airbrushing, etc. (within-group, by woman. Or, by queen, as in this example featuring Rhianna):
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