Seeing Ourselves Through Datasets: How AI is warping our media reflections
In 2023, Artificial Intelligence dominated discussions about authorship and ownership, the future of labour, corporate ethics, shifting legalese and expanding concepts of open culture. You can read about the ways Creative Commons has been supporting these important conversations here, here and here.
But I don’t want to talk about any of that today.
Today, I just want us to consider datasets. Inputs and Outputs. The ways “AI” systems’ are mediating access to our cultural commons, eroding diversity of thought and warping reflections of reality.
I am a collage artist and remixer of audio visual things. I am also an educator engaging young people in media literacy and critical thinking through creative expression. This learning is facilitated through engaging with the cultural commons, contributing CC0 licensed works to the public domain, remixing and creating meaning with found materials.
Our process begins with search. WHAT we find, WHERE we find it and HOW we find it frames the meaning found in the works we create.
Accessing the cultural commons contained in archives is becoming increasingly opaque as “search and find” processes are replaced with “asked and answered”, constricting our view of the larger context, pacifying our curiosity and limiting our imaginations.
When I use the terms US, WE and OURSELVES, I mean an ideal WE, one that includes all the WEs. It’s hard to imagine such a complete dataset. No such record of the entirety of our we- ness currently exists. The cultural commons is an idea as ever changing as culture itself. We are training AI systems to understand us based on wildly incomplete reflections of our reality. Then feeding those incomplete snapshots back into the training system, eliminating divergence of thought and flattening culture.
Finding Ourselves
When we begin online search and find missions, we quickly learn how many of “our” stories are missing from the visual record.The current state of image archives, accessible through platforms ranging from Getty Images to Wikimedia Commons, reveals significant omissions in representation.
Enter generative AI. Purporting to be the solution to all our search and find problems. Can’t find an image of “Ariel view of forest fire from a plane” in stock image libraries? AI can make it for you!
But what if I am looking for an image of youth as diverse as the ones living in Scarborough, Ontario for outreach materials that reflect those being engaged in an after school program?
While generative AI can create images based on words associated with media files within its datasets, it can NOT understand concepts. Any impression that it is conceptualizing is merely projective anthropomorphism, reflective of our profound relationship with communication.
AI systems recognize patterns and autofill sequential assumptions. A more complicated version of fill-in-the-blanks:
cat, kitten,
dog,...puppy
Guerrilla Girls, 1989
AI systems value quantity, having no way to analyze quality. The reality of online datasets heavily weighted towards representations of women rather than by women (from Venus of Willendorf to Pornhub) results in observable associations generative AI makes with images of women and the word beautiful.
The current state of the visual commons accessed through online search, as well as archives and collections of libraries and museums are rife with over and under-represented cultural understandings of race, gender, beauty norms, family structures, power systems, good taste, knowledge keepers, politics, on and on.
Critical thinking skills can help us to understand what media found in datasets reflect about society. But not if we can’t see them.
Because these AI platforms are run by media corporations concerned with their reputations, a public relations fuelled course-correction is being implemented to manipulate outcomes to be more pleasing and less reflective of the inadequacies of datasets they are trained on. Because each new version of these software replace the previous version, observing this change is reserved to early adopters.
Caption: below images, generated in DALLE
Can’t find image of young Black men that don’t reflect stereotypes? Generative-AI can produce those too! But should they?
This tweaking of outputs is a type of synthetic DEI (diversity, equity and inclusion), astroturfing over the social biases and media-information structures reflected by the gaps. It trains the AI to generate a surface representation of identities by mashing up concepts, understood through metadata/tags associated with images in its datasets.
Caption: images above were generated by an app using AI to create a professional headshot by having the user upload 10-15 pictures of themselves. In this case the individual uploading pictures similar to the one seen on the left, but was asked their race, which seemed to have an exaggerated role in the outputs.
Besides creating some pretty strange outputs, this synthetic source material robs communities of the opportunity for self representation.
Nothing about us without us.
When these performative outputs are then reintegrated into datasets, the distortion of reality affects the record of our past, present and future.
Knowing Ourselves
The datasets that train Large Language Models, determining the outputs of generative AI, are records of our past. Despite the misguided description of these outputs as novel, they are just the opposite. These records of our past are already limited by the many biases baked into systems governing how these records are indexed and ordered. SEO rankings are based on commercial forces, colonial systems outline the terms of the dewey decimal system, elitist gatekeepers determine what works of art are in “the canon”.
AI systems ranging from data-mining, algorithmic recommendations, chatbots and generative media, compound these issues.
Netflix’s algorithm recommends something similar to what you liked before and inevitably you are cornered into a niche of yesterday’s imagination, endlessly consuming increasingly narrow interpretations of what you liked last year.
As Jacob Ward outlines in his book “The Loop : How Technology is Creating a World Without Choices and How to Fight Back”, by pulling from records of our past, we compound these limited understanding of ourselves, tightening the loop between input and output, further limiting diversity of expression.
Expressing ourselves
Art and culture are expressions of meaning. An artist’s body of work is lexicon of its own. Generative AI swallows these languages whole and spits them out as a subheading, reducing their meaning to an aesthetic, a pattern, a colour-pallet, a motif.
Frida Khalo = jungle motif Escher = asymmetry Basquiat = ripped jeans
This is a threat to our understanding of the meaning contained within the visual commons and the cultural contributions of past creators. “In the style of” (Picasso) prompts can not be replaced with “in the spirit of”, or “containing similar meaning to” as AI systems have no frame of reference for interpreting such sentiments.
Distillation becomes distortion.
These issues are not new to media environments. 75% of people will never scroll past the first page on a Google search. Front page news and cover girls have always pulled focus from the icebergs of information under the surface. But as AI comes to dominate search, and overwhelming indexes (TLDR) become replaced with one answer from automated oracles, we move farther away from context, acknowledgement and the ability to see things differently.
Deciding for ourselves
After reading this, you might imagine I am against the use of AI. I am not. I am happy that we are past peak AI hype cycle after its explosive entrance into the mainstream in 2023. I am just getting started in experimenting with the technology myself, and I hope you join me/those of us who are. The noise is dying down and it’s time to get to work. It’s not a question of if but rather how AI systems will transform society. We need diversity of critique as well as imagination in order to do this.