This weekend we had perfect Spring weather in Southwestern Ontario. After an exciting (albeit exhausting) week attending my first symposium as a new Royal Roads student, working in my garden was the ideal way for me to relax and reflect on what I had learned in my first full week as a student in the MALAT (Masters of Arts in Learning and Technology) program. Spring is a time where I happily welcome the signs of life I missed so much during winter, new beginnings, and a fresh start. With that perspective, let’s consider what I’ve learned from this past week of discussions, student work presentations, and expert visits to our symposium.
Many of the pre-recorded sessions I attended reflected upon the benefits of Open Education, Open Educational Practices, and Open Educational Resources. Coming from a Corporate Learning & Development background this is a practice that I really struggle with still, especially since it’s been ingrained in me for 20 years that corporate privacy should be protected at all costs. Catherine Cronin’s recorded presentation Open Education, Open Questions (2017) caused me to question how open-minded I was to OEPs, especially after she presented a table showing different degrees of educational practice and my own experience and mindset fell mostly on the least open end of the spectrum (Cronin, 2017, 23:42 – 25:40). Even if my employer leverages closed educational practices, there are opportunities for me to be more open in terms of my educational persona online (such as reviving my professional twitter account) and looking for opportunities to employ OEPs in ways that would still conform to my organization’s privacy regulations.
One concept which surprised me a lot as the week went on was that I was becoming more open to how Generative AI technologies could be beneficial – not just harmful – in the world of educational technology. During our Link introductions I recall how optimistic my colleague Chris was about ChatGPT, and how much that shocked me. How could we possibly embrace this technology in education with the implications it has on academic dishonesty and ethical considerations? I had recently listened to a podcast on CBC Spark entitled ChatGPT and the future of AI (Young, 2023) which had left me full of uncertainty about the technology, believing it was something to be regulated and resisted. My mindset shifted ever so slightly after attending the AI and Learning Design panel, hearing Darren Wilson explain he encourages his students to use Generative AI to “art direct” (generate) the images they want to find instead of spending hours scrolling through image libraries (Childs et al., 2023, 11:45). Now, this is an idea I can get behind, as soon as we determine how to properly attribute the newly-generated image and accredit any artists whose creations influenced the Generative AI’s work.
As this week closes I find myself excited about what I’ve learned and curious to learn more – like one of the daffodils in my garden, just cracking through the surface of the soil.
References
Childs, E. et al. (2023, March 7). AI and Learning Design in Education [Webinar]. Royal Roads University. https://www.youtube.com/watch?v=IFrAs59sDHI.
Cronin, C. (2017, April 20). Open Culture, Open Education, Open Questions [Video]. MALAT Virtual Symposium 2017. https://malat-coursesite.royalroads.ca/lrnt521/catherine-cronin-choosing-open/.
Young, N. (Host). (2023, March 17). ChatGPT and the future of AI [Audio podcast]. In Spark. CBC Listen. https://www.cbc.ca/listen/live-radio/1-55-spark.
Wonderful thoughts – thanks!
So, a purposely provocative question for everyone regarding generative AI and attribution. If an AI creates a new work in the style of, say, Van Gough – but a totally different scene – should we expect an attribution of the style? How about if the AI combines styles such that the new creation could not really be said to reflect any particular style but is rather a unique approach informed by many others? And finally, would our answers differ if it was a young, emerging human artist in each of these scenarios?
At this point I don’t have specific answers or even a well-reasoned position, so just interested in everyone’s thoughts in this emerging area.
I really appreciate the thought-provoking question, Russ, and I’m looking forward to what others think about this. Here are my thoughts.
For me, when considering whether or not art created by AI is or is not plagiarism falls within the intent. Artists are often inspired by other artists, and may incorporate their style, colour palettes, brush strokes into their own original works of art. In those circumstances the intent is to still create a new piece of art which drew inspiration from other artists or styles. For example, my stepmum is a talented artist who draws a lot of inspiration from Impressionist painters like Monet, Renior, and Degas, but her intent is still to create an original piece of art. She would also freely tell you what inspired her painting.
For AI art I’m not seeing any transparency regarding what is influencing the new creation. If there were I might change my mind, but now at least the intention of these works of art is to be believable as an original creation. For example, last Fall I remember my teenager showing me a Tik Tok filter called MyHeritage Time Machine which would use pictures you uploaded to predict what you might look like during different periods in history (Antonelli, 2022). While I can’t find a citation for it now, I remember a few weeks later there was criticism that the MyHeritage tool had used other artists’ styles to influence the AI creations without giving them credit. This was a wildy popular trend which many people tried, yet those original artists never benefited from the increased exposure.
I found a recent article quoting Liz DiFiore from the (US) Graphic Artist Guild where she explained:
Until I see more transparency in AI art, or stronger copyright laws, I’m going to be inclined to err on the side of the artists and consider this sort of creation with a critical lens, leaning towards potential plagiarism.
(P.S. I’m not sure if I should be providing citations, but would it not be hypocritical if I didn’t given the topic?)
References
Antonelli, W. (2022). How to use myheritage’s AI Time Machine, a tool that shows what you would look like throughout history. Business Insider. https://www.businessinsider.com/guides/tech/myheritage-ai-time-machine#:~:text=However%2C%20MyHeritage's%20AI%20is%20a,others'%20work%20without%20their%20consent.
Nolan, B. (2022). Artists say AI image generators are copying their style to make thousands of new images – and it’s completely out of their control. Business Insider. https://www.businessinsider.com/ai-image-generators-artists-copying-style-thousands-images-2022-10
Here are a few simple musings around very complex issues. I bring up more questions than answers. I would agree with Andrea that intent and transparency play a big part in determining if use of another’s work is plagiarism. Human beings hold the responsibility to ensure they are following established laws and codes of conduct. We have that capacity, AI technologies do not, yet.
I found an article by Mark Coeckelbergh (2020) that enlightened me around the need to establish responsibility attribution, regarding both positive and negative intended and unintended outcomes resulting from the use artificial intelligence technologies. According to Coeckelbergh’s paper (2020, p16):
“Using the relational framework presented here, this requirement to exercise responsibility means that society deserves AI experts and operators who are in control, know what they are doing, and are able and willing to communicate, explain and give reasons for what they are doing to human and nonhuman moral patients. This includes the obligation to gain greater awareness of unintended consequences and the moral significance of what they do, including how they deal with tragic problems. If AI is not going to be responsible in this sense, it will crash”.
Where the lines get blurry for me is around creativity and inspiration as it then relates to attribution. Our brains grow and develop through environments, relationships and experiences. We consciously and unconsciously take in information and weave it into our values, beliefs and actions. Anything we produce is a culmination of all learning and experience. To quote Mark Twain:
“There is no such thing as a new idea. It is impossible. We simply take a lot of old ideas and put them into a sort of mental kaleidoscope. We give them a turn and they make new and curious combinations. We keep on turning and making new combinations indefinitely; but they are the same old pieces of colored glass that have been in use through all the ages.”
I feel that on some level, all artifacts, whether knowledge, physical art, fashion, music, technology, is a collaborative effort and a result of what came before. Once we share our ideas and creations with the world, they are going to inspire and evolve in the minds of those that internalize it. Which then leads to the messiness of open learning, and I’ll jump into Ano’s blog to add my thoughts to the discussion there as well.
Coeckelbergh, M. (2020). Artificial Intelligence, Responsibility Attribution, and a Relational Justification of Explainability. Sci Eng Ethics. https://doi.org/10.1007/s11948-019-00146-8
Twain, M. (1907). Mark Twain’s Own Autobiography: The Chapters from the North American Review. Retriebed from https://www.goodreads.com/work/quotes/23640909-chapters-from-my-autobiography
I think your Mark Twain quote (“There is no such thing as a new idea. It is impossible.”) captures just how complex this issue is, Lara. Each year we hear news reports of pop artists being accused of plaigarism for musical lyrics or melodies. Myself, when I’m creating an eLerarning module, I often look at what others have created first and borrow some of their ideas. When I did my Bachelor of Education degree (2004/2005… well before ChatGPT!) one of my professors said something to the effect of “a good teacher is a good thief. Borrow from others, make it your own, and give credit” (paraphrased, Dr. Brian Way)*. I think about this lesson from Dr. Way often, but always come back to the last part – give credit.
*I will admit I do not know if this is cited properly at all as I don’t recall the exact date, but it was something he told us often in our B. Ed. Intermediate/Senior English class.