(Image generated by Mid journey AI)
        (Command to
the AI- /Imagine a Beautiful Black Mum holding the children in an urban
area. Render 4k etc)
    
    The new 1000 Kenyan Shillings note has a
nickname used to refer to ladies of lighter skin tones… “Color ya Thao”:
This color, known informally as “Color ya Thao,” stands in for the idealized
light skin tone prized by many in our culture. That got me thinking: what if
the color codes employed in AI technology contribute to the perpetuation of
colorism biases and have an effect on our society as a whole.
    As a graduate student in communication
strategy, I’ve learned about the nuanced effects of colorism in the media. One
of our Professors stressed the need of writing or produce content for a
worldwide audience and use the internet to reach out to different demographics
yet seek to have local productions that appeal to the local population.
Because of this, I became interested in how AI affects our ideas of what is
attractive and desirable.
    Colorism, for the layperson, refers to the
discriminatory practice of treating someone differently because of their skin
tone. It frequently coincides with racism and shows up everywhere from the
workplace to private interactions. As a culture, we tend to favor those with
lighter skin tones, which contributes to the persistence of colorism.
    In light of recent research, it’s clear that
AI isn’t immune to inherent prejudices like racism. As an example, Google’s
computer vision algorithms now use the Monk Skin Tone (MST) scale to
categorize skin tones rather than the Fitzpatrick scale. The concept of “coded
bias,” in which racism is embedded in technology, prompted this change. Google
Photos incorrectly classifying black people as gorillas is just one example.
Racist soap dispensers and computer-generated stereotyped images are two
others. The Google skin lesion detection algorithm also did not work on those
with dark skin. Autonomous vehicles have been demonstrated to have trouble
identifying people of color compared to those with lighter skin tones,
according to research. (I have since learned that a lot of this is still in
research and corrections have been continuously updated)
    I decided to do an experiment with Mid journey
AI and a wide range of user-provided command prompts to learn more about the
effect of colorism on AI. Although the study’s findings are still being
reviewed, they show that there is a significant amount of work to be done to
eliminate colorism bias in AI.
Let’s explore the various images generated by command prompts:

(The command prompt by @Fantancy 2022- on Mid-journey AI was as follows-
romantic full-length portrait of a woman, a stunning woman in magical
white flowing embellished dress, flowers, crystals, and a handsome
modern man in evening wear…..)

    When I looked at the command prompt that
@Fantancy 2022 on Mid-journey AI had provided, I couldn’t help but note that
the produced image didn’t have very much variety in its appearance. There
was no hint of the skin tone that the generator had in mind for the prompt,
which asked for a passionate full-length portrait of a couple holding each
other’s hands. This has left me with a question: why did the people in the
picture only have white faces? Is it to imply that white people, in general,
are the only beautiful men and women?
Lets now examine the same command prompt with black added to it

    There is in fact a white face with light skin
included as part of the image that was made for an additional upgrade!
With 


DALL·E 2 – OpenAI

 was not any better.

To the industry experts:
ChatGPt postulates the following as a way of dealing with biases:


  1. Collect diverse and representative data: The data used to train an AI
    model should be diverse and representative of the population it will be
    used on. This is important to ensure that the model can perform well on
    a wide range of inputs and that it does not perpetuate existing biases.


  2. Pre-processing: Data pre-processing is important to ensure that the data
    is cleaned and ready for training. It includes removing outliers,
    duplicates, and irrelevant data, as well as handling missing values.


  3. Annotate data: Annotating data is critical for supervised learning. It
    is the process of adding labels or tags to the data, which the AI model
    uses to learn the relationship between inputs and outputs.


  4. Fairness evaluation: Use fairness metrics and evaluation methods to
    evaluate the performance of the model on different subgroups of the data
    to ensure that it does not perpetuate existing biases.


  5. Monitor and iterate: Monitor the model’s performance during and after
    training and make adjustments as needed. This may include collecting
    more data, adjusting the model’s architecture, or fine-tuning the
    model’s parameters.


  6. Explainability: Make sure that the model is interpretable and that its
    decisions can be explained. This is important to understand how the
    model makes its predictions and identify potential sources of bias.


  7. Ethical considerations: consider the ethical implications of using the
    model and how it could potentially harm certain groups of people.

 As communicators, we have a responsibility to advocate for a more fair
and inclusive future in which technology accurately represents the variety of
the world in which we live. It’s time for accurate storytelling and the
creation of images that include people of various races, ethnicities, and skin
colors. Let us paint a bright future for ourselves.
Signed:

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