My drawings are a reflection of my soul. What happens when artificial intelligence — and anyone with access to it — can replicate them?
Webcomic artist Sarah Andersen has written a timely Opinion for the New York Times on how The Alt-Right Manipulated My Comic. Then A.I. Claimed It.She talks about being harassed by the Alt-Right who created a shadow version of her work full of violent, racist and nazi motifs. Now she could be haunted by an AI-generated shadow like the image above. Her essay nicely captures the feeling of helplessness that many artists who survive on their work must be feeling before the “research” trick of LAION, the nonprofit arm of Stability AI that scraped copyrighted material under the cover of academic research and then made available for commercialization as Stable Diffusion.
Andersen links to a useful article on AI Data Laundering which is a good term for what researchers seem to be doing intentionally or not. What is the solution? Datasets gathered with consent? Alas too many of us, including myself, have released images on Flickr and other sites. So, as the article author Andy Baio puts it, “Asking for permission slows technological progress, but it’s hard to take back something you’ve unconditionally released into the world.”
While artists like Andersen may have no legal recourse that doesn’t make it ethical. Perhaps the academics that are doing the laundering should be called out. Perhaps we should consider boycotting such tools and hiring live artists when we have graphic design work.
A new art-generating AI system called Stable Diffusion can create convincing deepfakes, including of celebrities.
TechCrunch has a nice discussion of Deepfakes for all: Uncensored AI art model prompts ethics questions. The relatively sudden availability of AI text to art generators has provoked discussion on the ethics of creation and of large machine learning models. Here are some interesting links:
Ars Technica has another article on various projects to be able to see what original images might have been used in training AIs like MidJourney. Have AI image generators assimilated your art? New tool lets you check. The provenance of some of the training sets is documented here. It remains to be seen what you can do if your images have been used.
It is worth identifying some of the potential issues:
These art generating AIs may have violated copyright in scraping millions of images. Could artists whose work has been exploited sue for compensation?
The AIs are black boxes that are hard to query. You can’t tell if copyrighted images were used.
These AIs could change the economics of illustration. People who used to commission and pay for custom art for things like magazines, book covers, and posters, could start just using these AIs to save money. Just as Flickr changed the economics of photography, MidJourney could put commercial illustrators out of work.
We could see a lot more “original” art in situations where before people could not afford it. Perhaps poster stores could offer to generate a custom image for you and print it. Get your portrait done as a cyberpunk astronaut.
The AIs could reinforce visual bias in our visual literacy. Systems that always see Philosophers as old white guys with beards could limit our imagination of what could be.
These could be used to create pornographic deepfakes with people’s faces on them or other toxic imagery.
The genius of of Stéfan Sinclair who passed in August 2020. Voyant was his vision from the time of his dissertation for which he develop HyperPo.
The global team of people involved in Voyant including many graduate research assistants at the U of Alberta. See the About page of Voyant.
How Voyant built on ideas Stéfan and I developed in Hermeneutica about collaborative research as opposed to the inherited solitary paradigm.
How we have now developed an extension to Voyant called Spyral. Spyral is a notebook programming environment built on JavaScript. It allows you to document your Voyant explorations, save parameters for corpora and tools, preprocess texts, postprocess results, and create new visualizations. It is, in short, a full data analysis and visualization environment built into Voyant so you can easily call up and explore results in Voyant’s already rich tool set.
In the image above you can see a Spyral code cell that outputs two stacked graphs where the same pattern of words is graphed over two different, but synchronized, corpora. You can thus compare the use of the pattern over time between the two datasets.
Replication as a practice for recovering an understanding of innovative technologies now taken for granted like tokenization or the KWIC. I talked about how Stéfan and I have been replicating important text processing technologies as a way of understanding the history of computing and the digital humanities. Spyral was the environment we developed for documenting our replications.
I then backed up and talked about the epistemological questions about knowledge and knowledge things in the digital age that grew out of and then inspired our experiments in replication. These go back to attempts to think-through tools as knowledge things that bear knowledge in ways that discourse doesn’t. In this context I talked about the DIKW pyramid (data, information, knowledge, wisdom) that captures current views about the relationships between data and knowledge.
Finally I called for help to maintain and extend Voyant/Spyral. I announced the creation of a consortium to bring us together to sustain Voyant.
It was an honour to be able to give the Zampolli lecture on behalf of all the people who have made Voyant such a useful tool.
A short essay I wrote with Stéfan Sinclair on “Recapitulation, Replication, Reanalysis, Repetition, or Revivification” is now up in preprint form. The essay is part of a longer work on “Anatomy of tools: A closer look at ‘textual DH’ methodologies.” The longer work is a set of interventions looking at text tools. These came out of a ADHO SIG-DLS (Digital Literary Studies) workshop that took place in Utrecht in July 2019.
Our intervention at the workshop had the original title “Zombies as Tools: Revivification in Computer Assisted Interpretation” and concentrated on practices of exploring old tools – a sort of revivification or bringing back to life of zombie tools.
Visualizing ongoing stories of loss, adaptation and inequality
Scientific American has a whole issue on COVID that includes a collection of data visualizations, COVID’s Uneven Toll Captured in Data. The visualizations are fascinating, though some take careful reading.
Leonardo Impett has a nice demonstration here of ImageGraph: a visual programming language for the Visual Digital Humanities. ImageGraph is a visual programming environment that works with Google Colab. When you have your visual program you can compile it into Python in a Colab notebook and then run that notebook. The visual program is stored in your Github account and the Python code can, of course, be used in larger projects.
The visual programming language has a number of functions for handling images and using artificial intelligence techniques on them. It also has text functions, but they are apparently not fully worked out.
I love the way Impett combines off the shelf systems while adding a nice visual development environment. Very clean.
When a secretive start-up scraped the internet to build a facial-recognition tool, it tested a legal and ethical limit — and blew the future of privacy in America wide open.
The New York Times has an in depth story about Clearview AI titled, Facial Recognition: What Happens When We’re Tracked Everywhere We Go? The story tracks the various lawsuits attempting to stop Clearview and suggests that Clearview may well win. They are gambling that scraping the web’s faces for their application, even if it violated terms of service, may be protected as free speech.
The story talks about the dangers of face recognition and how many of the algorithms can’t recognize people of colour as accurately which leads to more false positives where police end up arresting the wrong person. A broader worry is that this could unleash tracking at another scale.
There’s also a broader reason that critics fear a court decision favoring Clearview: It could let companies track us as pervasively in the real world as they already do online.
The arguments in favour of Clearview include the challenge that they are essentially doing to images what Google does to text searches. Another argument is that stopping face recognition enterprises would stifle innovation.
The story then moves on to talk about the founding of Clearview and the political connections of the founders (Thiel invested in Clearview too). Finally it talks about how widely available face recognition could affect our lives. The story quotes Alvaro Bedoya who started a privacy centre,
“When we interact with people on the street, there’s a certain level of respect accorded to strangers,” Bedoya told me. “That’s partly because we don’t know if people are powerful or influential or we could get in trouble for treating them poorly. I don’t know what happens in a world where you see someone in the street and immediately know where they work, where they went to school, if they have a criminal record, what their credit score is. I don’t know how society changes, but I don’t think it changes for the better.”
It is interesting to think about how face recognition and other technologies may change how we deal with strangers. Too much knowledge could be alienating.
The story closes by describing how Clearview AI helped identify some of the Capitol rioters. Of course it wasn’t just Clearview, but also a citizen investigators who named and shamed people based on photos released.
Some of the things that struck me are the absence of medical terminology in the high frequency words. I was also intrigued by the prominence of “going to”. Trump spends a fair amount of time talking about what he and others are going to be doing rather than what is done. Here you have a Contexts panel from Voyant.
Do you need online teaching ideas and materials? Dialogica was supposed to be a text book, but instead we are adapting it for use in online learning and self-study. It is shared here under a CC BY 4.0 license so you can adapt as needed.
Dialogica (http://dialogi.ca) plays with the idea of learning through a dialogue. A dialogue with the text; a dialogue mediated by the tool; and a dialogue with instructors like us.
Dialogica is made up of a set of tutorials that students should be able to alone or with minimal support. These are Word documents that you (instructors) can edit to suit your teaching and we are adding to them. We have added a gloss of teaching notes. Later we plan to add Spyral notebooks that go into greater detail on technical subjects, including how to program in Spyral.
Dialogica is made available with a CC BY 4.0 license so you can do what you want with it as long as you give us some sort of credit.
