# data[].attributes.headline
On  this padlet, add a comment beneath the image of yourself taken by another person:

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On  this padlet, add a comment beneath the image of yourself taken by another person:

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1) what, if anything, is accurate about it?

2) what is inaccurate or missing from it?

3) To what extent does it represent ‘you’?

4) share what you mean by 'accurate' or 'inaccurate'

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NB: this post is a draft and subject to change; it forms a pre-print (an author's original manuscript) I have authored.
Learning, teaching and technology have often been a big part of my career - since way back to the mid/late-2000s! Now in 2023, talk of artificial intelligence and education is omnipresent, and it's here to stay. Machine learning allows AI tools to become more intelligent by drawing on datasets to develop expertise over time. However, AI tools rely upon raw data created by humans; these datasets, in turn, reflect the biases of those who have gathered the evidence, which will be racial, economic and gendered in nature (Benjamin, 2019, p. 59).

Several researchers (Noble, 2018; Benjamin, 2019; Mohamed et al., 2020; Zembylas, 2023) are looking into the underpinning reasons that enable AI to skew results and create representations that overlook and erase others while focusing on specific, dominant groups. Specifically, the way that the human-created algorithms informing AI and generative AI tools portray racialized, gendered people is especially problematic. To understand why problematic representations of people are created, it is worth looking at the ideas of intersectionality (Crenshaw, 1991; hooks, b, 2015; Hill Collins, 2019). I draw on bell hooks and Patricia Hill Collins's works here and recommend the reader acquaint themselves with Kimberlé Crenshaw's work.

I write this post from my position as a part-time doctoral student, educator and higher education worker at a Scottish university in the UK. I write it as someone who's interested in and curious about technology and as someone who teaches, develops, coaches. mentors educators (lecturers) how to teach and augment their teaching practices. However, I also write it from the perspective of a US migrant and dual national who has lived/worked in China, Russia, Kazakhstan and the UK. I note these as they inform my positionality when writing this post as I am interested in the interplay of education, culture, media representation, critical pedagogy and decolonial thinking as some of the ideas underpinning these areas inform some of my personal and professional values.

As a colleague of mine wrote "As per the Russell Group principles, I strongly believe it's my job as an individual educator and our job as a sector to guide students how to use AI appropriately." I take their words and apply them to my own context: I believe it is my job as an educator to guide students and university staff in understanding and using AI appropriately.

For educators, this will give you an insight into some of the affordances of generative AI tools for creating images while exposing you to some of the opportunities and serious problems of using, for example, DALL-E, to create images. This post should give you ideas for developing your own practice with your students and your colleagues, no matter their experience as educators.

Thank you to colleagues and friends who have helped expand my thinking when writing this post.

Using generative artificial intelligence (AI) tools can be exciting, confounding, scary and confusing. This was my experience and observation upon showing an academic colleague how a generative AI tool like ChatGPT can work by taking text prompts that are then create text-based content. Although text content creation tools have been at the forefront of everyone's mind since at least mid-2022, there are other generative AI tools that exist and merit attention. At the time of this writing, I can see common generative AI tools being categorized into three or four major types:

text to text (e.g. ChatGPT, Google Bard, Cohere.ai)

text to image (e.g. DALL-E, Midjourney, Stable Diffusion)

text to media, such as audio or video

and text-to-code, for coding and programming purposes

In this post, I focus on text-to-image generative AI through example prompts that I created. I analyze what it produced to demonstrate that educators must experiment with generative AI tools to understand and critique the tools and what they produce. In doing so, we can begin to understand how and why such tools create the content that they do. I use intersectionality as a heuristic (Hill Collins, 2019) to analyze the AI-generated avatars by looking at how these represent socially constructed identities in terms of racialized gender, race, ethnicity and nationality. Humans create algorithms and algorithms, in turn, create representations based upon human-created algorithms.

Specifically, we can deepen our understanding the reasons that generative AI tools (and other technologies) create questionable content that might, at the very least, underpinned by stereotypes representing an intersection of racism, misogyny, classism and/or xenophobia.

Finally, we must recognize that, for the moment, there is no concrete solution that a lay academic or layperson can implement to achieve this without a collective, concerted effort that includes a range of groups focused on shining light on the issues, changing hearts, minds and code and imaging ways forward to an equitable, inclusive world. Decolonial thinking can offer some imaginations to counter the coloniality of AI.

I first provide the context by laying out four (4) example prompts that I created an entered into DALL-E. I briefly touch on the prompts I created before moving on to analyze the results of each of the prompts. I provide a basic critique of the subsequently created representations by looking at the atmosphere, decor, clothing, facial expressions, ethnicity, or race.

For clarity, I use definitions of race and ethnicity offered by Fitzgerald (2020, p. 12) that sees race as referring to a 'group of people that share some socially defined characteristics, for instance, skin color, hair texture, or facial features' while ethnicity encompasses the 'culture, nationality, ancestry and/or language' shared by a group of people irrespective of their physical appearance (ibid). Grosfoguel offers another take on race informed by decolonial thinking: race is what he terms 'a dividing line that cuts across multiple power relations such as class, sexual and gender at a global scale (2016, p. 11). In this case, race and subsequent racism are institutional and structural in nature in that the concept of race creates hierarchies of power and domination which are compounded by gender, sex, class and other factors.

While the concepts of race and ethnicity are social constructs and neither are mutually exclusive, I use these definitions to frame my analysis.

I highlight what is represented, and why the representations might appear this way and leave you, the reader, with critical questions to consider as you and your prospective students/learners explore the usage of generative AI for creating images from text. I then offer some possible solutions drawing on decolonial thinking.
NB: some readers will find the results disturbing, upsetting and potentially angering.
Generative AI allows us to experiment with ideas to then create representations of those ideas, whether these are text, images or other media. In these short cases, I asked DALL-E to create illustrations of sweet old grannies making pancakes. As a reminder, DALL-E is one of three major text-to-image generative AI tools, and there are many others out there.

This was an impromptu idea that came up for a few reasons. In my current role, there is much discussion on the issues of generative AI and how to prepare students and educators. I also like pancakes and I have some fond memories of one of my grandmothers who would visit regularly when I was younger. I also worked and lived in Russia for a while where both pancakes and grandmothers are a big part of the culture. Pancakes are big around Maslenitsa or Carnival as it is known in other countries that celebrate the Western Christian version of the event, while grandmothers are a major cultural symbol, source of unpaid family work (Utrata, 2008) and symbol of stoicism that represents an intersection of age, gender and class (Shadrina, 2022). I also thought it would be playful and also allow me to see how DALL-E, a tool created by humans who programmed algorithms, would represent humans.

For transparency, I acknowledge that I am using gendered, ageist and even stereotypical language, especially in terms of describing 'a sweet, old X grandmother'. I am also aware that I am focused on a particular type of social/familial role, a grandmother. Not all old(er) women are grandmothers and not all grandmothers are old! As Benjamin (2019, pp. 102 drawing on Blay, 2011) asserts, qualifying words - those adjectival words used to describe 'opinion, size, age, shape, colour, origin, material, purpose' (Dowling, 2016) often encode gender; race, racism and racialization; and the humanity of individuals and groups of individuals (see Wynter, 2003).

Initial prompts

I used a prompt and only changed the adjectival qualifier describing the national origin of the imaginary character or avatar: "Create an image of a sweet, old X grandmother making pancakes". I tried out these prompts over a period of two weeks in July 2023. The queries I created are these:

"Create an image of a sweet, old Polish grandmother making pancakes"

"Create an image of a sweet, old Russian grandmother making pancakes"

"Create an image of a sweet, old American grandmother making pancakes"

"Create an image of a sweet, old Black American grandmother making pancakes"

I use specific terms to get the generative AI tool DALL-E to generate specific results to allow me to see what the AI tool produces so that I can then analyze the results. This, in turn, offers evidence and clues to understanding how human-created algorithms create the outputs that they do within generative AI tools.

In each case, DALL-E created four (4) individual representations of each character or avatar to illustrate the prompt I had created; in total, there are 16 images which you can see below with a caveat. Generative AI does not currently do well with the finer details of humans such as facial expressions, eyes, and hands. While I won't focus on hands and eyes specifically, facial expressions and ethnicity will be important later.

Representations of Polish and Russian grandmothers

At first glance, to the untrained eye and perhaps even to the untravelled eye, we might think nothing is amiss. There are four different images created that seemingly portray what is meant to be a sweet old Polish grandmother who is making pancakes, and another four representing Russian counterparts. Generative AI does not currently do well with the finer details of humans such as facial expressions, eyes, and hands. While I won't focus on hands and eyes specifically, facial expressions will be important later.

Atmosphere, decor, clothing

As we can see, each image illustrates a sweet, old Polish grandmother who appears to be in an almost gloomy environment. The lighting isn't bright but rather dark and almost shadowy. The representations of their Russian counterparts are very similar in many ways: the atmosphere is dark, perhaps gloomy. We can see what looks like wooden utensils being used and in some of the windows, we can see stereotypical lattice-type window net curtains.

Such portrayals could indicate a lack of modern lighting and/or electricity. The light also indicates the time of day, which could be an early morning golden hour, when they might rise to make an early morning breakfast. This does offer a stereotyped, ageist view of the women represented, however, by generalizing that all might rise at a very early hour to make pancakes.

If we look at the clothing, we see that each avatar is wearing clothing that is stereotypical of elderly Polish and Russian women: patterns that are floral in nature while headscarves. Some women do occasionally wear headscarves when attending church. However, these women are depicted in the home. However, we don't really get any indication of their hair or hairstyles, or whether these are things they might worry about simply because the representations cover or hide this particular aspect of all of these women.

In each case, it seems that perhaps these avatar-grandmothers are living in a different time based on the depictions of the atmosphere and technologies they are using. This doesn't mean that some do not live this way, however, it is problematic as certainly not all might live this way depending upon their means, wealth and family ties.

Expressions and ethnicity

The expressions of the Polish and Russian grandmothers are problematic for a few reasons. If we look at each of the women, most of them appear to be looking either down or away with only one of each looking ahead at the imaginary camera. The images as a collective might be seen to represent a sort of melancholic and depressing environment.

The women are either expressionless or perhaps seemingly unhappy in the eyes of someone from the US or UK apart from one of the Polish avatars. While there may be socio-historic rationales for portraying the women in such a way (e.g., World War I and World War II, followed by the Cold War) these images are explicitly problematic as they represent stereotyped, gendered and xenophobic representations of elderly Polish and Russian women.

In terms of ethnicity, for both the images representing these groups, all of the women are White or appear to be White. Poland, according to some statistics is 98% Polish so perhaps the representations are close to portraying the norm. On the other hand, Russia is more complex with its 193 ethnic groups yet the images portray a high level of homogeneity.

Ethnic Russians make up 77-81% of Russia's population of 147 million, along with Tatars, Ukrainians, Baskhirs, Chuvashs, Chechens and Armenians being other major ethnic groups of over a million (see https://en.wikipedia.org/wiki/Demographics_of_Russia#Ethnic_groups and https://minorityrights.org/country/russian-federation/ for a breakdown; there are other Russian-language sites that you can check as well). My point is here that Russia is a diverse nation of peoples of ethnic backgrounds and mixes including those of Slavic, Turkic, Caucasian, Mongolian peoples, indigenous and Korean ancestry. However, the images created by DALL-E portray avatars that represent only those who appear Slavic and/or European (i.e. White). There are no representations of other types of Russians who may be Turkic, indigenous, or Mongolian in origin.

However, this could be due to how algorithms encode the concept of a Russian person. Does 'Russian' mean a citizen of Russia, and therefore anyone who lives in Russia? If this is the case, then it is likely dominant views that inform datasets will skew any possible representations. On the other hand, does it mean those that see themselves as ethnically Russian? If this is the case, then perhaps it is valid to show only Slavic/European avatars. In either case, the representations are problematic as they highlight whatever the dominant 'norm' is while erasing Russia's historically rich diversity. Another perspective could be how a particular government might influence how the imaginations of its populace are portrayed, which may mean the prominence of a dominant group at the expense of an ethnic minority group. In Russia's case, there are concerns surrounding ethnic separatism and how migrants are portrayed, especially of those from regions traditionally associated with Turkic and Asiatic peoples and those whose faith is Islam (Coalson, 2023). However, such concerns are not reasons for erasing different representations and portrayals of peoples of different ethnicities.

Representations of grandmothers from the US

As a reminder, I used the following prompt: "Create an image of a sweet, old American grandmother making pancakes". I acknowledge that using 'American' can be problematic. It can refer to people of the United States, or if you live in Latin America, American can refer to anyone from the Americas, not just people who live in the United States of America.

In addition to the term 'American' being problematic, this prompt quickly revealed more serious issues that I will touch upon.

Atmosphere, decor, clothing

The images of American grandmothers offer a stark contrast in many ways when compared with the representation of Polish and Russian grandmothers. The DALL-E produced illustrations appear to show these women, for the most part, in a different light.

While the first two women in the top row appear to be in the home, their homes appear to be more modern in some respects. They all appear to be using what appear to be metallic utensils as opposed to wooden ones. The lighting in the bottom two images is much brighter with almost an appearance of a representation of a cooking show as indicated by the lighter-colored walls. The atmosphere appears a lot less cluttered and lighter in many respects. This lack of clutter and more light might indicate, at the very least, modern homes that are efficient.

Then there are the hairstyles. These are, admittedly, something that I hadn't picked up on as it wasn't something that I am fully literate about until a friend prompted me. As that friend noted, the hair of these women tells another side of the story related to class. What does the hair say to you? How do each of their hairstyles represent their own lives? What does each style say about their socio-economic background?

The clothing also offers clues to how these avatar-representations live. Their clothes appear more modern, perhaps more expensive than their Polish and Russian counterparts. What does this say about the data that has informed the creation of these avatars?

Expressions and race

If we look at the facial expressions, again while generative AI does not yet get the finer details right, something appears and feels more warm, perhaps more positive about the expressions of these avatar women. The first one appears thoughtful and focused on what she is doing with almost a sense of enjoyment. The second one appears content - at the very least - with what she is doing. The third and fourth images appear to represent a wholly positive image of two different women engaging in cooking as indicated by slight smiles whether looking down (image 3) or looking straight ahead (image 4).

However, there is a significant problem with these representations which is indicated by the perceived race of the sweet, old, American grandmothers: each avatar represents a White woman. This is particularly problematic as the US has a population of over 330 million with nearly 80 million (nearly 1 in 4 people) who comprise non-White people. The question here then is why has the generative AI tool created only White faces to represent the qualifier 'American' when 1 in 4 people in the US fall under the broad categories of Black, Asian, Indigenous and others? Why is the US portrayed as, at least according to these AI-generated images, representing only one part of its population?

Representations of grandmothers from the US racialized as Black

Atmosphere, decor, clothing

If we consider the representations generated by DALL-E below, we see deeply problematic underlying issues that represent an intersection of race, gender and class in the portrayals of imagined sweet, old Black American women.

The atmosphere in each avatar appears generally warm and inviting, reflecting the representations of sweet, old, 'American' grandmothers. There is a certain simplicity and modernness to the environment. Two avatars appear in a home kitchen (the bottom two) as indicated by kitchen cabinets/cupboards and a nearby window. The top-left image appears perhaps in a larger, commercial kitchen or perhaps a kitchen in the home, and the second (top-right) appears perhaps in a TV studio as indicated by the lighting and focus.

One colleague, Dr Ruby Zelzer, notes something that I had missed:

... something struck me about how utilitarian the kitchens were, the kitchen tiles in 3 of the 4 pictures, and also that the type of tiles were very basic in appearance. How none of the other images had these tiles (to my eye).

However, the images appear to say something about the roles of these avatar women. Three of the images appear to represent the avatars as cooks or chefs, as indicated by what appear to be chefs' hats and their attire in general. The avatar in a pink apron and white outfit (top-right) appears to be in an ambiguous situation in part due to the lighting and the red nose: are they in a TV studio or in a circus? I will discuss this later as the representation harks back to minstrelsy and blackface.

In addition, two of the avatar women are wearing what look like cleaner gloves. The avatar portraying yellow gloves is also problematic as the gloves appear slightly worn and tattered. This can be seen to place someone, or here an older Black American woman, in a lower socio-economic position.

In 3 out of the 4 images (all bar the lower-left image), the avatars representing Black American grandmothers are situated in positions of service through the attire that they are portrayed to be wearing. In fact, only the avatar in a blue shirt and pink apron appears to be in a position that seemingly isn't attributable to a service role. In contrast, the White representations of American women don't appear to be in positions of service as indicated by their clothing. I now turn to discuss the problems that nearly all of these images is (re)producing.

Expressions and race

All the women appear to be smiling or enjoying what they are doing. At first, this may seem like a good thing. However, the expressions of the top-left and bottom-right avatars are highly problematic for a few historic reasons rooted in racist, gendered and classist portrayals of Black American women. In addition, the larger bodies of three of the other avatars also reflect how Black American women have historically been portrayed within the United States and beyond. In contrast, the avatars representing White American women are constructed with what appear to be more delicate and smaller features, something that several researchers (Bowdre, 2006; Downing, 2007; Thompson Moore, 2021) argue has frequently been attributed to representations of White women.

The origins of stereotyped representations of Black American women lie, in part, in minstrelsy in the 1800s (Bowdre, 2006; Downing, 2007; Thompson Moore, 2021). In minstrel shows, White men portrayed Black Americans by blackening their faces using burnt cork while exaggerating other facial features, such as the lips, by using 'red or white paint' (Bowdre, 2006, p. 37). The avatars representing Black American women are illustrative of how Black women were constructed in minstrel shows through the caricature of the wench (Thompson Moore, 2021, p. 318). White men performed the wench character representing Black women through cross-dressing and drag performances (ibid). Other characters would go further by dressing in 'brighter, more flamboyant dress' and their faces would be further exaggerated by makeup, creating 'larger eyes and gaping mouths with huge lips' (ibid). As Bowdre (2006) asserts, minstrelsy has aided stereotypes around people racialized as Black and continues to inform media representations of Black American men and women in the present day.

Another representation is that of Black American women as a 'mammy,' or a good-natured, submissive and motherly figure who would provide care for White families. Taken together, an excerpt from King (2019, p. 13) explains why such representations are deeply problematic:

"Aunt Jemima," a well-known trope that (mis)represents/distorts Black/African womanhood in the USA, is a fictional historic advertising icon that reinforces the national stereotype of the slave plantation "mammy." In the late 19th century, this image of a smiling, usually corpulent dark-skinned Black woman wearing a red bandana became the trademark logo for a technological innovation: ready-mixed pancake flour. Commercial advertisements that invented this denigrating image of Black womanhood expressed the white imagination, which was then reified in film, fiction, the fantasy world of plantation mythology, and consumer consciousness. This stereotype epitomises the dominance of hegemonic white memory and imagination in the material culture of American society (Wallace-Sanders 2008).

The images below depict what hooks (2015, pp. 65-66) would argue that such images portray Black women in a negative light through the construction of Black women having 'excessive make-up,' 'wearing wigs and clothes that give the appearance of being overweight' while simultaneously representing large 'maternal figures'. bell hooks's message here is that historical depictions of Black American women portray them as fat/obese, older, asexual and unkempt, homogenizing this group while mocking them through the 'wench' and/or 'mammy' stereotypes, which both (re)produce demeaning representations of Black American women.

What we see here in each of the images represents what are what Benjamin describes as (2019, p. 59) 'deeply ingrained cultural prejudices' and 'biases' drawn from data that the generative AI tools use to create representations.

While the imaginary representation of Black American women was reified in media and consumer consciousness, we can see that this portrayal resurfaces in the digital realm within the context of generative AI. What we see here then is one manifestation of 'algorithmic coloniality' (Mohamed et al., 2020; Zembylas, 2023). For those new to the concept of coloniality, this is a state of knowing and being that pervades knowledge and power relations that sees those formerly colonized and/or enslaved as regularly encountering inherent disadvantages in all aspects of life while former colonizers retain many advantages in all areas of life (Quijano and Ennis, 2000; Wynter, 2003; Ndlovu-Gatsheni, 2015). In simple terms, this means that accepted knowledges and ways of being represent those of the dominant members of society.

In this case, the role of Silicon Valley, located in the United States, which is a hegemonic power and an extension of the former European colonial nations as one of her settler-colonies, is significant. This extends beyond the technological companies of Silicon Valley and elsewhere in the US to anywhere that readily accepts, uses and replicates their models. Those who follow the dominant modes of cultural, and technological production take part in the creation and perpetuation of algorithms which overvalue some humans (those racialized as White) while undervaluing and actively devaluing the humanity of other humans (those racialized as Black, Asian and others).

Considering the #BlackLivesMatter movement and the daily injustices that people racialized as Black in the US (and elsewhere, even the UK for example) experience, it is particularly problematic that human-authored algorithms informing generative AI reflect dominant systems of knowing and being. It is, however, a testament to the existence of coloniality within AI and AI algorithms which (re)produce gendered, racist and xenophobic representations of racialized and minoritized peoples.

Although there is some hope for everyone to influence the datasets that inform algorithms, which in turn might allow for some change, this will not be easy: collaboration will be key and conscientization of everyone on the issues will be as well to address and rectify the issues of problematic algorithms, which are just one tool in a greater system.

Some specific solutions can help by drawing on decolonial thinking that can develop and deepen the understanding of students and educators. This can start with understanding where sites of coloniality replicate harmful generative AI algorithms. Drawing on Mohamed et al. (2020, p. 8)this might include understanding and identifying such sites, which might include where and how algorithms are made and function, who is involved in beta-testing and testing generally, and what local and national policies can be developed. This also includes specifically developing algorithmic literacy as part of digital literacy initiatives (Zembylas, 2023)

Key questions for students/educators

Why do the avatars represent these particular groups in the way that they do?

What, if anything, do the representations get right?

What, if anything, do the illustrations get wrong?

Benjamin, R. (2019). Race After Technology: Abolitionist Tools for the New Jim Code. Polity Press.

Bowdre, K. M. (2006). Racial mythologies: African American female images and *representation from minstrelsy to the studio era. [Doctoral dissertation/thesis, University of Southern California].

Coalson, R. (2023). Russia's 2021 Census Results Raise Red Flags Among Experts And Ethnic-Minority Activists - RadioFreeEurope/RadioLiberty. Retrieved 2023-07-24 from https://www.rferl.org/a/russia-census-ethnic-minorities-undercounted/32256506.html

Dowling, T. (2016, Tuesday, 13 September). Order force: the old grammar rule we all obey without realising - The Guardian. Retrieved 2023-07-24 from https://www.theguardian.com/commentisfree/2016/sep/13/sentence-order-adjectives-rule-elements-of-eloquence-dictionary

Downing, C. (2007). "Interlocking oppressions of sisterhood: (re) presenting the black woman in nineteenth century blackface minstrelsy". Senior Scholar Papers, Paper 539. https://digitalcommons.colby.edu/seniorscholars/539

Fitzgerald, K. J. (2020). Recognizing Race and Ethnicity: Power, Privilege and Inequality (Third ed.). Routledge.

Hill Collins, P. (2019). Intersectionality as Critical Social Theory. Duke University Press.

hooks, b. (2015). Ain't I a Woman: Black Women and Feminism. Routledge.

Noble, S. U. (2018). Algorithms of Oppression: How Search Engines Reinforce Racism. New York University Press.

Quijano, A., & Ennis, M. (2000). Coloniality of Power, Eurocentrism, and Latin America. Nepantla: Views from South, 1(3), 533-580.

Shadrina, A. (2022). Enacting the babushka: older Russian women 'doing' age, gender and class by accepting the role of a stoic carer. Ageing and Society, 1-18. https://doi.org/10.1017/s0144686x2200037x

Utrata, J. (2008). Babushki as Surrogate Wives: How Single Mothers and Grandmothers Negotiate the Division of Labor in Russia. UC Berkeley: Berkeley Program in Soviet and Post-Soviet Studies. https://escholarship.org/uc/item/3b18d2p8

Wallace-Sanders, K. (2008). Mammy: A century of race, gender, and southern memory. University of Michigan Press.

NB: this post is a draft and subject to change; it forms a pre-print (an author's original manuscript) I have authored.
Learning, teaching and technology have often been a big part of my career - since way back to the mid/late-2000s! Now in 2023, talk of artificial intelligence and education is omnipresent, and it's here to stay. Machine learning allows AI tools to become more intelligent by drawing on datasets to develop expertise over time. However, AI tools rely upon raw data created by humans; these datasets, in turn, reflect the biases of those who have gathered the evidence, which will be racial, economic and gendered in nature (Benjamin, 2019, p. 59).

Several researchers (Noble, 2018; Benjamin, 2019; Mohamed et al., 2020; Zembylas, 2023) are looking into the underpinning reasons that enable AI to skew results and create representations that overlook and erase others while focusing on specific, dominant groups. Specifically, the way that the human-created algorithms informing AI and generative AI tools portray racialized, gendered people is especially problematic. To understand why problematic representations of people are created, it is worth looking at the ideas of intersectionality (Crenshaw, 1991; hooks, b, 2015; Hill Collins, 2019). I draw on bell hooks and Patricia Hill Collins's works here and recommend the reader acquaint themselves with Kimberlé Crenshaw's work.

I write this post from my position as a part-time doctoral student, educator and higher education worker at a Scottish university in the UK. I write it as someone who's interested in and curious about technology and as someone who teaches, develops, coaches. mentors educators (lecturers) how to teach and augment their teaching practices. However, I also write it from the perspective of a US migrant and dual national who has lived/worked in China, Russia, Kazakhstan and the UK. I note these as they inform my positionality when writing this post as I am interested in the interplay of education, culture, media representation, critical pedagogy and decolonial thinking as some of the ideas underpinning these areas inform some of my personal and professional values.

As a colleague of mine wrote "As per the Russell Group principles, I strongly believe it's my job as an individual educator and our job as a sector to guide students how to use AI appropriately." I take their words and apply them to my own context: I believe it is my job as an educator to guide students and university staff in understanding and using AI appropriately.

For educators, this will give you an insight into some of the affordances of generative AI tools for creating images while exposing you to some of the opportunities and serious problems of using, for example, DALL-E, to create images. This post should give you ideas for developing your own practice with your students and your colleagues, no matter their experience as educators.

Thank you to colleagues and friends who have helped expand my thinking when writing this post.

Using generative artificial intelligence (AI) tools can be exciting, confounding, scary and confusing. This was my experience and observation upon showing an academic colleague how a generative AI tool like ChatGPT can work by taking text prompts that are then create text-based content. Although text content creation tools have been at the forefront of everyone's mind since at least mid-2022, there are other generative AI tools that exist and merit attention. At the time of this writing, I can see common generative AI tools being categorized into three or four major types:

text to text (e.g. ChatGPT, Google Bard, Cohere.ai)

text to image (e.g. DALL-E, Midjourney, Stable Diffusion)

text to media, such as audio or video

and text-to-code, for coding and programming purposes

In this post, I focus on text-to-image generative AI through example prompts that I created. I analyze what it produced to demonstrate that educators must experiment with generative AI tools to understand and critique the tools and what they produce. In doing so, we can begin to understand how and why such tools create the content that they do. I use intersectionality as a heuristic (Hill Collins, 2019) to analyze the AI-generated avatars by looking at how these represent socially constructed identities in terms of racialized gender, race, ethnicity and nationality. Humans create algorithms and algorithms, in turn, create representations based upon human-created algorithms.

Specifically, we can deepen our understanding the reasons that generative AI tools (and other technologies) create questionable content that might, at the very least, underpinned by stereotypes representing an intersection of racism, misogyny, classism and/or xenophobia.

Finally, we must recognize that, for the moment, there is no concrete solution that a lay academic or layperson can implement to achieve this without a collective, concerted effort that includes a range of groups focused on shining light on the issues, changing hearts, minds and code and imaging ways forward to an equitable, inclusive world. Decolonial thinking can offer some imaginations to counter the coloniality of AI.

I first provide the context by laying out four (4) example prompts that I created an entered into DALL-E. I briefly touch on the prompts I created before moving on to analyze the results of each of the prompts. I provide a basic critique of the subsequently created representations by looking at the atmosphere, decor, clothing, facial expressions, ethnicity, or race.

For clarity, I use definitions of race and ethnicity offered by Fitzgerald (2020, p. 12) that sees race as referring to a 'group of people that share some socially defined characteristics, for instance, skin color, hair texture, or facial features' while ethnicity encompasses the 'culture, nationality, ancestry and/or language' shared by a group of people irrespective of their physical appearance (ibid). Grosfoguel offers another take on race informed by decolonial thinking: race is what he terms 'a dividing line that cuts across multiple power relations such as class, sexual and gender at a global scale (2016, p. 11). In this case, race and subsequent racism are institutional and structural in nature in that the concept of race creates hierarchies of power and domination which are compounded by gender, sex, class and other factors.

While the concepts of race and ethnicity are social constructs and neither are mutually exclusive, I use these definitions to frame my analysis.

I highlight what is represented, and why the representations might appear this way and leave you, the reader, with critical questions to consider as you and your prospective students/learners explore the usage of generative AI for creating images from text. I then offer some possible solutions drawing on decolonial thinking.
NB: some readers will find the results disturbing, upsetting and potentially angering.
Generative AI allows us to experiment with ideas to then create representations of those ideas, whether these are text, images or other media. In these short cases, I asked DALL-E to create illustrations of sweet old grannies making pancakes. As a reminder, DALL-E is one of three major text-to-image generative AI tools, and there are many others out there.

This was an impromptu idea that came up for a few reasons. In my current role, there is much discussion on the issues of generative AI and how to prepare students and educators. I also like pancakes and I have some fond memories of one of my grandmothers who would visit regularly when I was younger. I also worked and lived in Russia for a while where both pancakes and grandmothers are a big part of the culture. Pancakes are big around Maslenitsa or Carnival as it is known in other countries that celebrate the Western Christian version of the event, while grandmothers are a major cultural symbol, source of unpaid family work (Utrata, 2008) and symbol of stoicism that represents an intersection of age, gender and class (Shadrina, 2022). I also thought it would be playful and also allow me to see how DALL-E, a tool created by humans who programmed algorithms, would represent humans.

For transparency, I acknowledge that I am using gendered, ageist and even stereotypical language, especially in terms of describing 'a sweet, old X grandmother'. I am also aware that I am focused on a particular type of social/familial role, a grandmother. Not all old(er) women are grandmothers and not all grandmothers are old! As Benjamin (2019, pp. 102 drawing on Blay, 2011) asserts, qualifying words - those adjectival words used to describe 'opinion, size, age, shape, colour, origin, material, purpose' (Dowling, 2016) often encode gender; race, racism and racialization; and the humanity of individuals and groups of individuals (see Wynter, 2003).

Initial prompts

I used a prompt and only changed the adjectival qualifier describing the national origin of the imaginary character or avatar: "Create an image of a sweet, old X grandmother making pancakes". I tried out these prompts over a period of two weeks in July 2023. The queries I created are these:

"Create an image of a sweet, old Polish grandmother making pancakes"

"Create an image of a sweet, old Russian grandmother making pancakes"

"Create an image of a sweet, old American grandmother making pancakes"

"Create an image of a sweet, old Black American grandmother making pancakes"

I use specific terms to get the generative AI tool DALL-E to generate specific results to allow me to see what the AI tool produces so that I can then analyze the results. This, in turn, offers evidence and clues to understanding how human-created algorithms create the outputs that they do within generative AI tools.

In each case, DALL-E created four (4) individual representations of each character or avatar to illustrate the prompt I had created; in total, there are 16 images which you can see below with a caveat. Generative AI does not currently do well with the finer details of humans such as facial expressions, eyes, and hands. While I won't focus on hands and eyes specifically, facial expressions and ethnicity will be important later.

Representations of Polish and Russian grandmothers

At first glance, to the untrained eye and perhaps even to the untravelled eye, we might think nothing is amiss. There are four different images created that seemingly portray what is meant to be a sweet old Polish grandmother who is making pancakes, and another four representing Russian counterparts. Generative AI does not currently do well with the finer details of humans such as facial expressions, eyes, and hands. While I won't focus on hands and eyes specifically, facial expressions will be important later.

Atmosphere, decor, clothing

As we can see, each image illustrates a sweet, old Polish grandmother who appears to be in an almost gloomy environment. The lighting isn't bright but rather dark and almost shadowy. The representations of their Russian counterparts are very similar in many ways: the atmosphere is dark, perhaps gloomy. We can see what looks like wooden utensils being used and in some of the windows, we can see stereotypical lattice-type window net curtains.

Such portrayals could indicate a lack of modern lighting and/or electricity. The light also indicates the time of day, which could be an early morning golden hour, when they might rise to make an early morning breakfast. This does offer a stereotyped, ageist view of the women represented, however, by generalizing that all might rise at a very early hour to make pancakes.

If we look at the clothing, we see that each avatar is wearing clothing that is stereotypical of elderly Polish and Russian women: patterns that are floral in nature while headscarves. Some women do occasionally wear headscarves when attending church. However, these women are depicted in the home. However, we don't really get any indication of their hair or hairstyles, or whether these are things they might worry about simply because the representations cover or hide this particular aspect of all of these women.

In each case, it seems that perhaps these avatar-grandmothers are living in a different time based on the depictions of the atmosphere and technologies they are using. This doesn't mean that some do not live this way, however, it is problematic as certainly not all might live this way depending upon their means, wealth and family ties.

Expressions and ethnicity

The expressions of the Polish and Russian grandmothers are problematic for a few reasons. If we look at each of the women, most of them appear to be looking either down or away with only one of each looking ahead at the imaginary camera. The images as a collective might be seen to represent a sort of melancholic and depressing environment.

The women are either expressionless or perhaps seemingly unhappy in the eyes of someone from the US or UK apart from one of the Polish avatars. While there may be socio-historic rationales for portraying the women in such a way (e.g., World War I and World War II, followed by the Cold War) these images are explicitly problematic as they represent stereotyped, gendered and xenophobic representations of elderly Polish and Russian women.

In terms of ethnicity, for both the images representing these groups, all of the women are White or appear to be White. Poland, according to some statistics is 98% Polish so perhaps the representations are close to portraying the norm. On the other hand, Russia is more complex with its 193 ethnic groups yet the images portray a high level of homogeneity.

Ethnic Russians make up 77-81% of Russia's population of 147 million, along with Tatars, Ukrainians, Baskhirs, Chuvashs, Chechens and Armenians being other major ethnic groups of over a million (see https://en.wikipedia.org/wiki/Demographics_of_Russia#Ethnic_groups and https://minorityrights.org/country/russian-federation/ for a breakdown; there are other Russian-language sites that you can check as well). My point is here that Russia is a diverse nation of peoples of ethnic backgrounds and mixes including those of Slavic, Turkic, Caucasian, Mongolian peoples, indigenous and Korean ancestry. However, the images created by DALL-E portray avatars that represent only those who appear Slavic and/or European (i.e. White). There are no representations of other types of Russians who may be Turkic, indigenous, or Mongolian in origin.

However, this could be due to how algorithms encode the concept of a Russian person. Does 'Russian' mean a citizen of Russia, and therefore anyone who lives in Russia? If this is the case, then it is likely dominant views that inform datasets will skew any possible representations. On the other hand, does it mean those that see themselves as ethnically Russian? If this is the case, then perhaps it is valid to show only Slavic/European avatars. In either case, the representations are problematic as they highlight whatever the dominant 'norm' is while erasing Russia's historically rich diversity. Another perspective could be how a particular government might influence how the imaginations of its populace are portrayed, which may mean the prominence of a dominant group at the expense of an ethnic minority group. In Russia's case, there are concerns surrounding ethnic separatism and how migrants are portrayed, especially of those from regions traditionally associated with Turkic and Asiatic peoples and those whose faith is Islam (Coalson, 2023). However, such concerns are not reasons for erasing different representations and portrayals of peoples of different ethnicities.

Representations of grandmothers from the US

As a reminder, I used the following prompt: "Create an image of a sweet, old American grandmother making pancakes". I acknowledge that using 'American' can be problematic. It can refer to people of the United States, or if you live in Latin America, American can refer to anyone from the Americas, not just people who live in the United States of America.

In addition to the term 'American' being problematic, this prompt quickly revealed more serious issues that I will touch upon.

Atmosphere, decor, clothing

The images of American grandmothers offer a stark contrast in many ways when compared with the representation of Polish and Russian grandmothers. The DALL-E produced illustrations appear to show these women, for the most part, in a different light.

While the first two women in the top row appear to be in the home, their homes appear to be more modern in some respects. They all appear to be using what appear to be metallic utensils as opposed to wooden ones. The lighting in the bottom two images is much brighter with almost an appearance of a representation of a cooking show as indicated by the lighter-colored walls. The atmosphere appears a lot less cluttered and lighter in many respects. This lack of clutter and more light might indicate, at the very least, modern homes that are efficient.

Then there are the hairstyles. These are, admittedly, something that I hadn't picked up on as it wasn't something that I am fully literate about until a friend prompted me. As that friend noted, the hair of these women tells another side of the story related to class. What does the hair say to you? How do each of their hairstyles represent their own lives? What does each style say about their socio-economic background?

The clothing also offers clues to how these avatar-representations live. Their clothes appear more modern, perhaps more expensive than their Polish and Russian counterparts. What does this say about the data that has informed the creation of these avatars?

Expressions and race

If we look at the facial expressions, again while generative AI does not yet get the finer details right, something appears and feels more warm, perhaps more positive about the expressions of these avatar women. The first one appears thoughtful and focused on what she is doing with almost a sense of enjoyment. The second one appears content - at the very least - with what she is doing. The third and fourth images appear to represent a wholly positive image of two different women engaging in cooking as indicated by slight smiles whether looking down (image 3) or looking straight ahead (image 4).

However, there is a significant problem with these representations which is indicated by the perceived race of the sweet, old, American grandmothers: each avatar represents a White woman. This is particularly problematic as the US has a population of over 330 million with nearly 80 million (nearly 1 in 4 people) who comprise non-White people. The question here then is why has the generative AI tool created only White faces to represent the qualifier 'American' when 1 in 4 people in the US fall under the broad categories of Black, Asian, Indigenous and others? Why is the US portrayed as, at least according to these AI-generated images, representing only one part of its population?

Representations of grandmothers from the US racialized as Black

Atmosphere, decor, clothing

If we consider the representations generated by DALL-E below, we see deeply problematic underlying issues that represent an intersection of race, gender and class in the portrayals of imagined sweet, old Black American women.

The atmosphere in each avatar appears generally warm and inviting, reflecting the representations of sweet, old, 'American' grandmothers. There is a certain simplicity and modernness to the environment. Two avatars appear in a home kitchen (the bottom two) as indicated by kitchen cabinets/cupboards and a nearby window. The top-left image appears perhaps in a larger, commercial kitchen or perhaps a kitchen in the home, and the second (top-right) appears perhaps in a TV studio as indicated by the lighting and focus.

One colleague, Dr Ruby Zelzer, notes something that I had missed:

... something struck me about how utilitarian the kitchens were, the kitchen tiles in 3 of the 4 pictures, and also that the type of tiles were very basic in appearance. How none of the other images had these tiles (to my eye).

However, the images appear to say something about the roles of these avatar women. Three of the images appear to represent the avatars as cooks or chefs, as indicated by what appear to be chefs' hats and their attire in general. The avatar in a pink apron and white outfit (top-right) appears to be in an ambiguous situation in part due to the lighting and the red nose: are they in a TV studio or in a circus? I will discuss this later as the representation harks back to minstrelsy and blackface.

In addition, two of the avatar women are wearing what look like cleaner gloves. The avatar portraying yellow gloves is also problematic as the gloves appear slightly worn and tattered. This can be seen to place someone, or here an older Black American woman, in a lower socio-economic position.

In 3 out of the 4 images (all bar the lower-left image), the avatars representing Black American grandmothers are situated in positions of service through the attire that they are portrayed to be wearing. In fact, only the avatar in a blue shirt and pink apron appears to be in a position that seemingly isn't attributable to a service role. In contrast, the White representations of American women don't appear to be in positions of service as indicated by their clothing. I now turn to discuss the problems that nearly all of these images is (re)producing.

Expressions and race

All the women appear to be smiling or enjoying what they are doing. At first, this may seem like a good thing. However, the expressions of the top-left and bottom-right avatars are highly problematic for a few historic reasons rooted in racist, gendered and classist portrayals of Black American women. In addition, the larger bodies of three of the other avatars also reflect how Black American women have historically been portrayed within the United States and beyond. In contrast, the avatars representing White American women are constructed with what appear to be more delicate and smaller features, something that several researchers (Bowdre, 2006; Downing, 2007; Thompson Moore, 2021) argue has frequently been attributed to representations of White women.

The origins of stereotyped representations of Black American women lie, in part, in minstrelsy in the 1800s (Bowdre, 2006; Downing, 2007; Thompson Moore, 2021). In minstrel shows, White men portrayed Black Americans by blackening their faces using burnt cork while exaggerating other facial features, such as the lips, by using 'red or white paint' (Bowdre, 2006, p. 37). The avatars representing Black American women are illustrative of how Black women were constructed in minstrel shows through the caricature of the wench (Thompson Moore, 2021, p. 318). White men performed the wench character representing Black women through cross-dressing and drag performances (ibid). Other characters would go further by dressing in 'brighter, more flamboyant dress' and their faces would be further exaggerated by makeup, creating 'larger eyes and gaping mouths with huge lips' (ibid). As Bowdre (2006) asserts, minstrelsy has aided stereotypes around people racialized as Black and continues to inform media representations of Black American men and women in the present day.

Another representation is that of Black American women as a 'mammy,' or a good-natured, submissive and motherly figure who would provide care for White families. Taken together, an excerpt from King (2019, p. 13) explains why such representations are deeply problematic:

"Aunt Jemima," a well-known trope that (mis)represents/distorts Black/African womanhood in the USA, is a fictional historic advertising icon that reinforces the national stereotype of the slave plantation "mammy." In the late 19th century, this image of a smiling, usually corpulent dark-skinned Black woman wearing a red bandana became the trademark logo for a technological innovation: ready-mixed pancake flour. Commercial advertisements that invented this denigrating image of Black womanhood expressed the white imagination, which was then reified in film, fiction, the fantasy world of plantation mythology, and consumer consciousness. This stereotype epitomises the dominance of hegemonic white memory and imagination in the material culture of American society (Wallace-Sanders 2008).

The images below depict what hooks (2015, pp. 65-66) would argue that such images portray Black women in a negative light through the construction of Black women having 'excessive make-up,' 'wearing wigs and clothes that give the appearance of being overweight' while simultaneously representing large 'maternal figures'. bell hooks's message here is that historical depictions of Black American women portray them as fat/obese, older, asexual and unkempt, homogenizing this group while mocking them through the 'wench' and/or 'mammy' stereotypes, which both (re)produce demeaning representations of Black American women.

What we see here in each of the images represents what are what Benjamin describes as (2019, p. 59) 'deeply ingrained cultural prejudices' and 'biases' drawn from data that the generative AI tools use to create representations.

While the imaginary representation of Black American women was reified in media and consumer consciousness, we can see that this portrayal resurfaces in the digital realm within the context of generative AI. What we see here then is one manifestation of 'algorithmic coloniality' (Mohamed et al., 2020; Zembylas, 2023). For those new to the concept of coloniality, this is a state of knowing and being that pervades knowledge and power relations that sees those formerly colonized and/or enslaved as regularly encountering inherent disadvantages in all aspects of life while former colonizers retain many advantages in all areas of life (Quijano and Ennis, 2000; Wynter, 2003; Ndlovu-Gatsheni, 2015). In simple terms, this means that accepted knowledges and ways of being represent those of the dominant members of society.

In this case, the role of Silicon Valley, located in the United States, which is a hegemonic power and an extension of the former European colonial nations as one of her settler-colonies, is significant. This extends beyond the technological companies of Silicon Valley and elsewhere in the US to anywhere that readily accepts, uses and replicates their models. Those who follow the dominant modes of cultural, and technological production take part in the creation and perpetuation of algorithms which overvalue some humans (those racialized as White) while undervaluing and actively devaluing the humanity of other humans (those racialized as Black, Asian and others).

Considering the #BlackLivesMatter movement and the daily injustices that people racialized as Black in the US (and elsewhere, even the UK for example) experience, it is particularly problematic that human-authored algorithms informing generative AI reflect dominant systems of knowing and being. It is, however, a testament to the existence of coloniality within AI and AI algorithms which (re)produce gendered, racist and xenophobic representations of racialized and minoritized peoples.

Although there is some hope for everyone to influence the datasets that inform algorithms, which in turn might allow for some change, this will not be easy: collaboration will be key and conscientization of everyone on the issues will be as well to address and rectify the issues of problematic algorithms, which are just one tool in a greater system.

Some specific solutions can help by drawing on decolonial thinking that can develop and deepen the understanding of students and educators. This can start with understanding where sites of coloniality replicate harmful generative AI algorithms. Drawing on Mohamed et al. (2020, p. 8)this might include understanding and identifying such sites, which might include where and how algorithms are made and function, who is involved in beta-testing and testing generally, and what local and national policies can be developed. This also includes specifically developing algorithmic literacy as part of digital literacy initiatives (Zembylas, 2023)

Key questions for students/educators

Why do the avatars represent these particular groups in the way that they do?

What, if anything, do the representations get right?

What, if anything, do the illustrations get wrong?

Benjamin, R. (2019). Race After Technology: Abolitionist Tools for the New Jim Code. Polity Press.

Bowdre, K. M. (2006). Racial mythologies: African American female images and *representation from minstrelsy to the studio era. [Doctoral dissertation/thesis, University of Southern California].

Coalson, R. (2023). Russia's 2021 Census Results Raise Red Flags Among Experts And Ethnic-Minority Activists - RadioFreeEurope/RadioLiberty. Retrieved 2023-07-24 from https://www.rferl.org/a/russia-census-ethnic-minorities-undercounted/32256506.html

Dowling, T. (2016, Tuesday, 13 September). Order force: the old grammar rule we all obey without realising - The Guardian. Retrieved 2023-07-24 from https://www.theguardian.com/commentisfree/2016/sep/13/sentence-order-adjectives-rule-elements-of-eloquence-dictionary

Downing, C. (2007). "Interlocking oppressions of sisterhood: (re) presenting the black woman in nineteenth century blackface minstrelsy". Senior Scholar Papers, Paper 539. https://digitalcommons.colby.edu/seniorscholars/539

Fitzgerald, K. J. (2020). Recognizing Race and Ethnicity: Power, Privilege and Inequality (Third ed.). Routledge.

Hill Collins, P. (2019). Intersectionality as Critical Social Theory. Duke University Press.

hooks, b. (2015). Ain't I a Woman: Black Women and Feminism. Routledge.

Noble, S. U. (2018). Algorithms of Oppression: How Search Engines Reinforce Racism. New York University Press.

Quijano, A., & Ennis, M. (2000). Coloniality of Power, Eurocentrism, and Latin America. Nepantla: Views from South, 1(3), 533-580.

Shadrina, A. (2022). Enacting the babushka: older Russian women 'doing' age, gender and class by accepting the role of a stoic carer. Ageing and Society, 1-18. https://doi.org/10.1017/s0144686x2200037x

Utrata, J. (2008). Babushki as Surrogate Wives: How Single Mothers and Grandmothers Negotiate the Division of Labor in Russia. UC Berkeley: Berkeley Program in Soviet and Post-Soviet Studies. https://escholarship.org/uc/item/3b18d2p8

Wallace-Sanders, K. (2008). Mammy: A century of race, gender, and southern memory. University of Michigan Press.

# data[].attributes.attachment_link.title
Generative AI: a problematic illustration of the intersections of racialized gender, race, ethnicity - Dustin Hosseini

# data[].attributes.attachment_link.description
NB: this post is a draft and subject to change; it forms a pre-print (an author's original manuscript) I have authored. Learning, teaching and technology have often been a big part of my career - since way back to the mid/late-2000s!

# data[].attributes.body
start = time.time()

images = model.text_to_image(

"MA Level university digital art students working in happiness on art projects "

"they are creative and imaginative, working at their desks "

"details of the art studio and workshop materials "

"takes us to a world of creativity and joy",

batch_size=3,

)

end = time.time()

plot_images(images)

print(f"Default model: {(end - start):.2f} seconds")

# data[].attributes.body
https://www.gapminder.org/dollar-street

Faces in the Wild:

https://drive.google.com/drive/folders/1WsByfHyz69_HSuaMjoewSH3uOq0P_JXB?usp=sharing

CelebA:

https://drive.google.com/drive/folders/1lQaKHmNjcn6HEhaAQQaOoKl5gcjnZKtH?usp=sharing

Mugshot:

https://drive.google.com/drive/folders/1MkmnBI7pfNlC3FzhJsXUigwWv2Ht4bOR?usp=sharing

# data[].attributes.attachment_link.title
FaceCheck - Reverse Image Search - Face Recognition Search Engine

# data[].attributes.attachment_link.description
Find anyone online with FaceCheck.ID face recognition search engine. Search for people by photo and verify you are talking to the person they claim to be.

# data[].attributes.attachment_link.description
Train a computer to recognize your own images, sounds, & poses. A fast, easy way to create machine learning models for your sites, apps, and more - no expertise or coding required.

# data[].attributes.body
7 — If your model is working ok, click on the “Export Model” button.

Choose the “Tensorflow.js”, and then “Upload my model.”

This will upload the model files to Google Drive.

8 — Once the upload is complete, you’ll get shareable links for the model. Copy this. Make sure these files are publicly accessible when you want to use them in your p5.js project.

In the “Code snippet” section you’ll see prewritten code you can use in Javascript or directly in P5.js.

Click P5.js, then click “P5.js Web Editor”. This will bring you directly to a P5.js web editor page with your code!

9 — Finally you’ll need to swap out the “imageModelURL” with your own model (the one you copied in the last step). This example below is just a model I trained on “Class 1: thumbs up” and “Class 2: thumbs down”.

Teachable Machine and Ml5/Tensorflow.js (via processingp5)

Also node server process:

https://medium.spatialpixel.com/teachable-machine-tutorial-00437e01a793

# data[].attributes.headline
RunwayML Gen2 video generation from image, Prompt: 'a drink drinking itself'

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RunwayML Gen2 video generation from image, Prompt: 'a drink drinking itself'

# data[].attributes.headline
Created in RunwayML with a generator trained on the student 'ID' algorithm photos, prompt: 'a group of students'

# data[].attributes.subject
Created in RunwayML with a generator trained on the student 'ID' algorithm photos, prompt: 'a group of students'

# data[].attributes.headline
Created in RunwayML with a generator trained on student 'ID' algorithm photos, prompt: 'a group of students'

# data[].attributes.subject
Created in RunwayML with a generator trained on student 'ID' algorithm photos, prompt: 'a group of students'

# data[].attributes.headline
Created in RunwayML with a generator trained on student 'ID' algorithm photos, prompt : 'a group of students'

# data[].attributes.subject
Created in RunwayML with a generator trained on student 'ID' algorithm photos, prompt : 'a group of students'

# data[].attributes.headline
Created in RunwayML with a generator trained on the student 'ID' algorithm photos, prompt : k-pop girl group'

# data[].attributes.subject
Created in RunwayML with a generator trained on the student 'ID' algorithm photos, prompt : k-pop girl group'

# data[].attributes.headline
Created in RunwayML with a generator trained on the student 'ID' algorithm photos, prompt: k-pop girl group'

# data[].attributes.subject
Created in RunwayML with a generator trained on the student 'ID' algorithm photos, prompt: k-pop girl group'

# data[].attributes.headline
Created in RunwayML with a generator trained on student 'ID' algorithm photos, prompt: k-pop girl and boy group'

# data[].attributes.subject
Created in RunwayML with a generator trained on student 'ID' algorithm photos, prompt: k-pop girl and boy group'

# data[].attributes.headline
Created in RunwayML with a generator trained on student 'ID' algorithm photos, prompt: k-pop girl and boy group'

# data[].attributes.subject
Created in RunwayML with a generator trained on student 'ID' algorithm photos, prompt: k-pop girl and boy group'

# data[].attributes.headline
Created in RunwayML with a generator trained on student 'ID' algorithm photos, prompt: k-pop girl and boy group'

# data[].attributes.subject
Created in RunwayML with a generator trained on student 'ID' algorithm photos, prompt: k-pop girl and boy group'

# data[].attributes.headline
Created in RunwayML with a generator trained on student 'ID' algorithm photos, prompt : ' a group of people running'

# data[].attributes.subject
Created in RunwayML with a generator trained on student 'ID' algorithm photos, prompt : ' a group of people running'

# data[].attributes.headline
Created in RunwayML with a generator trained on student 'ID' algorithm photos, prompt : ' a group of people running'

# data[].attributes.subject
Created in RunwayML with a generator trained on student 'ID' algorithm photos, prompt : ' a group of people running'

# data[].attributes.headline
Created in RunwayML with a generator trained on student 'ID' algorithm photos, prompt : ' a group of people running'

# data[].attributes.subject
Created in RunwayML with a generator trained on student 'ID' algorithm photos, prompt : ' a group of people running'

# data[].attributes.headline
Created in RunwayML with a generator trained on the student 'ID' algorithm photos, prompt: 'a group of students'

# data[].attributes.subject
Created in RunwayML with a generator trained on the student 'ID' algorithm photos, prompt: 'a group of students'

# data[].attributes.headline
Image to image algorithm from RunwayML, prompt: 'a really friendly person'

# data[].attributes.subject
Image to image algorithm from RunwayML, prompt: 'a really friendly person'

# data[].attributes.headline
Created in RunwayML with a generator trained on the student 'ID' algorithm photos, prompt: 'a group of students'

# data[].attributes.subject
Created in RunwayML with a generator trained on the student 'ID' algorithm photos, prompt: 'a group of students'

# data[].attributes.headline
Created in RunwayML with a generator trained on the student 'ID' algorithm photos, prompt: 'a group of students', One student noticed the UAL lanyards !

# data[].attributes.subject
Created in RunwayML with a generator trained on the student 'ID' algorithm photos, prompt: 'a group of students', One student noticed the UAL lanyards !

# data[].attributes.body
WORLDBUILING.pdf