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Human-Machine
Collaboration
Using art-making AI (CrAIyon) as
cited work, or inspiration, reference, base
Shalin Hai-Jew
Kansas State University

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Presentation Overview
Generative AI can create natural language text in various formats and
voices and perspectives (ChatGPT) and emulative style-transferred
visual images (CrAIyon, DALL-E, MidJourney, and others).
Controversies are swirling around various aspects of generative AI.
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Presentation Overview (cont.)
How the generative AI tools are made and run:
• uses of copyrighted seeding texts and visuals in databases to train the
AIs (without the permission of the original authors, in some cases),
• guardrails around generative contents (such as those against x-rated
content, against hate speech, against various dimensioned
stereotypes, etc.) vs. those without any guardrails,
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Presentation Overview (cont.)
How the generative AI tools are used:
• academic honesty, citations,
• commercial applications,
• authorship and crediting (and rewards and liabilities)
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Presentation Overview(cont.)
• A typical sequence in using a text-seeded generative AI that creates
digital visuals is to use seeding phrases to describe the desired visual
(often multiple iterations)…selecting the image…downloading the
image as a .webp (“weppy”) format (neither raster nor vector), and
directly using the image with citation…or using the image as an
inspiration, reference, or base. (One creates a derived image, by
borrowing some visual concepts from the generative AI.)
“Photorealistic” asks the AI for create an image that looks like an
actual photo. “after Picasso” or “in the style of Georgia Totto
O’Keeffe” asks for a style transfer from the known works of the artist
into a different context.
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Presentation Overview (cont.)
• [Please see “Co-Creating Common Art with the CrAIyon AI” on
SlideShare: https://www.slideshare.net/ShalinHaiJew/cocreating-
common-art-with-the-craiyon-ai for a clearer visual gist of this
phenomenon.]
• The presenter wrote an article titled “CrAIyon: Putting an art-making
AI through its paces” on the C2C Digital Magazine at
(https://scalar.usc.edu/works/c2c-digital-magazine-fall-2022---winter-
2023/craiyon-paces). As a former college faculty member and current
instructional designer, the presenter introduces the topic and throws
a conversation around the complexities.
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Giving CrAIyon a Roll
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What are some text prompts that you might
use with CrAIyon?
• Remember that this tool enables a wide range of languages and
symbols and combinations.
• This tool can engage in “style transfer,” so if you have a particular
visual artist you like, you can evoke that artist’s name.
• You can evoke various materials that you want the generative AI to
produce.
• Go a few rounds. Iterate from your original text prompt. Or try some
highly variant prompts.
• Any visuals worth downloading (.webp)?
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Impressions? (first or otherwise)
• What are you noticing about the machine-generated visuals?
• Did anyone try “photorealistic” as one of their prompts?
• Did anyone try “after” a particular artist style?
• Did anyone try a particular material effect?
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Impressions? (first or otherwise) (cont.)
• Do you feel like you could export something practically useful?
Impractically useful?
• What do you like? Why?
• What do you dislike? Why?
• What are some practical applications for you?
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Impressions? (first or otherwise) (cont.)
• Are you starting to notice some visual tropes that the AI has?
• Whare are some strengths of the generative AI (based on
impressions)? What are some weaknesses?
• If you could change the tool capabilities, what would you do, and
why?
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Debate around Human-Machine
“Collaboration”…and Credit
In terms of generative AI
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Generative AI…
• …refers to computational programs that emulate humans by
generating various content (text, audio, imagery, motion visuals,
video, and other elements, including combines audiovisual ones)
• The AI programs learn through large samples of big data, so it can
emulate nuances of understandings…although such programs are in
early years (some compare them to “teenagers”).
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Generative AI (cont.)
• Generative adversarial networks (GANs) do not only generate
contents to particular objectives, but they have a built-in test of
quality / veracity to desired outcomes against which the generative
aspect strives to improve.
• Generative AI can generate content into ∞ and beyond, but there
may not be enough human attention (attention economy) to enjoy
the various works. The works need to be meaningful to humans.
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Human in the Loop
• As it stands, the generative AIs may be prompted in a mix of ways:
• Textual prompts to help the AI know what is desirable in terms of visuals, so it
can output various visuals for consideration
• Visual prompts to help the AI have examples of the visual gist and perhaps
content that may be desirable, so it can output various visuals for
consideration
• Combined inputs (textual and visual)
• And others
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Debate around Human-Machine
“Collaboration”
• How much is the human responsible for the artwork vs. the
computing machine?
• Does the computing machine have an agentic role given its design as
“artificial intelligence”?
• Does it matter if the work is run unedited directly from the generative
AI download?
• Does it matter if the work is edited and changed, superficially?
• Does it matter if the work is edited and changed, down to the pixel level?
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Debate around Human-Machine
“Collaboration” (cont.)
• Who has the first idea (remember that the machine can turn out
something serendipitous and unconceptualized by the person who
inputs the prompt)?
• What if the content (text or visual) is created sui generis (unique, one-of-a-
kind) by the computing machine?
• What if the content (text or visual) is created ex nihilo (from nothing,
specifically without inputs by people’s works or by people’s prompts)?
• Who is responsible for the aesthetics? The composition? The social
angle? And why?
• Who is the “animating agent” and why?
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…And Credit
Human
• Traditionally, artists are credited
for original single-artist and
team-created fine artworks.
• The artist’s name and
personality and history serve as
an artist brand.
Machine
• Can AI be credited for (fine and
other) artworks?
• What are the implications for
renown? For money? For
payment? For prizes?
• Should the programmers behind
an art-making AI be credited for
the artworks?
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…And Reward…and Liability
Human
• In tradition, the artist bears
responsibility for the work to
some degree…and then the
owner thereafter…
Machine
• In tradition, computing
machines do not generally bear
reward or liability. Rather, their
owners do.
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Reapportionment of Credit? Rewards?
Liabilities?
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• Should credit be reapportioned? Rewards? Liabilities?

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Drawing Lines…Or Not…
• How should schools address generative AI?
• How should professions? (How can people show their value
especially while in competition with generative AI works?)
• How should publishers?
• Or is this too soon to start drawing lines?
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What Would You Do, and Why?
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Q1: How would you use generative AI in your
respective contexts?
• And then, do you feel the need to disclose that you use an AI for
assistance or some other reason?
• Do you count neural filters (in digital image editing) as AI (after all,
various neural networks were used to tune the digital image editing, to
transfer styles, to recolor photos, to change hue / saturation, to change
the depth-of-field, to enable deep zoom, to enable facial editing, and
others)?
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Q2: If you teach or edit, what would your
policies be about generative AI? Why?
• Explain why your policy is timely…but fair…and pro-learner / pro-
author (or researcher).
• How would you differentiate something that is created from
generative AI vs. not?
• What are ways to manage this issue of generative AI in teaching and
learning?
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Q3: What can learners learn from generative
AI if it is harnessed as a learning tool?
• Given the state of the world, with advanced technologies coming to
the fore in the 4IR (Fourth Industrial Revolution), how can people
adapt and better position themselves for these fast-arriving changes?
• How would you as a learner harness generative AI?
• Are there ways to make generative AI more friendly for learners?
• Much of generative AI has been democratized, with free web-facing portals
and apps. Are there other ways to extend the benefits to the world?
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Q4: What are some ways to position for the
impact of generative AI on human jobs?
• Generative AI is thought to squelch some white-collar jobs while
creating other jobs. What do you see as the possible impact of
generative AI on your particular work and professional circumstance?
Why?
• If you have funds to retrain, how would you use those funds?
• What are some other AIs and technologies on the horizon that may
well have a big impact on the near-future? The mid-term future?
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Q5: Any questions for the group that have not
yet been asked?
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My Own Usage of CrAIyon
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Some Real-Life Applications
Practical: Visuals in an Open-Source,
Open-Access Magazine
• An open-source and open-access
publication needs some light visuals
to break up the gray text.
• I have gone through social imagery
datasets and not found anything that
the author likes.
• I go to generative AI to output a few
ideas, and then I create a visual image
to use with the generated image as an
inspiration or a reference image (used
for its lines which I capture via trace).
Impractical: Amusement,
Entertainment
• I want to be amused. I can go to a
generative AI with various text prompts
to see what it will output. Some can be
highly surprising and funny. (Generative
AIs can have a strange sense of physics
and bodies and hands and eyes.)
• A generative AI can offer high
entertainment value.
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Some Real-Life Applications(cont.)
Practical: Learning about Digital Image
Editing
• I am practicing digital image editing. I
want to learn new functions in some
very complex digital image editing
software.
• I create some unusual prompts, and
then I use the imagery to apply new
learning.
• I also want to learn more about
AI…and how it interacts with language.
Impractical: Open-Sharing of Social
Imagery
• I want to create visuals for open-
sharing on the Social Web. I
conceptualize an idea…and generate
some images…and then use those
images to serve as references for an
original visual.
• Anything used more directly is
credited as from AI.
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Some Real-Life Applications(cont.)
Practical: Learning about Visual
Thinking
• In other times, I want to learn how to
create moods and other visual effects.
The generative AI can offer fresh
insights.
• Ditto with various visual
communications messages…some
banal…and some more creative…
• Sometimes, I want to understand
word definitions in a visual sense.
Impractical: Wasting Time
• In between work, sometimes, it helps
to just put in a text prompt and have
the generative AI doodle something
(in under 2 minutes).
• Sometimes, there is the additional
benefit of learning…but a distraction
is sometimes very welcome.
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Some Initial Personal Observations
• There is not a straight-line to the visual I usually want, which tends to
be somewhat artsy and strange.
• Iterating from an initial prompt with additional prompts may not get me much
closer.
• Word order only seems to matter sometimes.
• It does help to know the lingo for digital image editing and analog image
making (sketching, drawing, painting, mixed media, and others).
• The generative AI is too often literal and less symbolic and less
figurative. It is almost never poetic.
• Occasionally, there is a fluke that can result in something very
aesthetically pleasing and unexpected.
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Some Initial Personal Observations (cont.)
• The generative AI, CrAIyon, IMHO, has its own quirks and signatures
(based on my half-year of experiences with it).
• It draws famous (and non-famous people badly, with weird eyes and bad hands
when it is trying to be “photorealistic”.
• It has a hard time drawing human proportions, too.
• It draws animals poorly, without a real-world sense of anatomy.
• It blurs much of a small image. (Blurring reads as non-committal.)
• It seems to like spirals to stand in for something precise.
• It does not do clean gridlines.
• It does not seem to do gradients.
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Some Initial Personal Observations (cont.)
• It seems to have some bias towards spooky things.
• It sometimes goes full-bore stereotypical.
• It sometimes pulls people in its own quirky universe instead of aligning with the
real world.
• Between figurative and abstract, it seems to do the middle ground fairly well
but not those on the extreme ends.
• It does not handle text (probably there is some throttling back against
the uses of any language…so it has a doodle-y version of its own).
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Some Initial Personal Observations (cont.)
Some of my purposeful and unpurposeful misadventures:
• When I typed in “fairytales for men,” the AI generated 9 visuals of
men in various fashion walking down catwalks.
• When I typed in “wild,” the generative AI came up with a variety of
apparent mammals that do not exist except in its own imagination.
These are patchwork animals, with patches of skin, some horns,
different hooves, and so on.
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Some Initial Personal Observations (cont.)
• Truth to tell, I am mostly generally impressed and appreciative.
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The Upsides
• So to end on an upside, I like CrAIyon because of the following:
• It is always game. Whatever prompt I put in, it always kicks up something.
• It outputs visuals I can practically use in most cases.
• It sometimes outdoes itself and offers me something that is easy to make look
artful. [But this is after years of my work in the digital image editing space.]
• It is surprising now and again.
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The Upsides (cont.)
• While it has challenges with some shapes, it composites well.
• It handles values (lights and darks) well.
• I think it’s working on its color sensibilities. Often, it can be quite
muted. It does not take risks in its color choices.
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The Upsides (cont.)
• I for one will keep right on learning…because it helps to be ready.
And this saves so much on time and analog art materials. Well, I do
have some time sinks where I spend an hour digitally editing a work
until I am satisfied.
• We should test limits…
• My uses of generative AI (currently) are for “artwork” with a lower-
case “a,” nothing aspiring to high art. My uses are for common art.
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A Caveat
• Generative AI really does make my life somewhat easier when I have
to chase images, but the jury (general public, decision makers, and
others) is still out about the uses of generative AI-supported
visuals…so perhaps we need to see how the world shapes out in
terms of its approaches.
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Conclusion and Contact
• Dr. Shalin Hai-Jew
• Instructional Design
• ITS
• Kansas State University
• 785-532-5262
• shalin@ksu.edu
• Note: All the visuals in the slideshow (save the cross-functional
diagram) were seeded with CrAIyon visuals but edited pretty heavily
in Adobe Illustrator and Adobe Photoshop. Some could have run
unedited, but I wanted to change these up from the originals.
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