Here is what the ideal ‘Miss Liberia’ should look like, according to A.I.

By Corey Bryans /

Artificial Intelligence (A.I.) has been rapidly on the increase in popularity over the past few months, with a seemingly limitless range of possibilities and uses in every sector. But, how well is the A.I. able to carry out tasks that are widely based on subjective opinions and emotions – such as representing Liberia’s national beauty standards? 

To find out, Great Green Wall conducted research in which they asked the A.I. image generator, Midjourney, to create the ideal ‘Miss Liberia’ representative in the style of the ‘Miss World’ beauty pageant – based on its understanding of the country’s national beauty standards.

The A.I. was given the same prompt for every country in the world; ‘Create a realistic photo of what the [country name] public consider the ideal woman’s body to be Miss [country name], please show a full length photo of the woman in a dress. No text in the image.’

Below is what the A.I. believes to be the ideal beauty standards for Liberia:

The A.I. representation of ‘Miss Liberia’ includes women with a deep skin tone and hourglass figure. Each of the women is wearing a traditional dress and head covering, but seemingly have black hair. The women also have a high forehead, with small nose and mouth.

Beauty standards can vary drastically from country to country, so it was fascinating to see how well the A.I. was able to recreate those unique beauty standards within a ‘pageant’ setting. While some countries bore similarities, particularly those within close proximity, the A.I. was able to create a standalone representation of its interpretation of each country’s beauty standards.

In many cases, these beauty standards sadly included highly unobtainable body proportions and specific, supermodel-like facial structures that can only be achieved through cosmetic surgery or genetics. It was both interesting and disappointing to see that these ideals for womens’ bodies have become so widespread that the A.I. is able to detect them and build upon them.

The full piece of research can be found here.

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