Thanks to the Digital Humanities Advancement Grant we were awarded by the National Endowment for the Humanities, our Data Lifeboat project (which is part of the Content Mobility Program) is now well and truly underway. The Data Lifeboat is our response to the challenge of archiving the 50 billion or so images currently on Flickr, should the service go down. It’s simply too big to archive as a whole, and we think that these shared histories should be available for the long term, so we’re exploring a decentralized approach. Find out more about the context for this work in our first blog post.

So, after our kick-off last month, we were left with a long list of open questions. That list became longer thanks to our first all-hands meeting that took place shortly afterwards! It grew again once we had met with the project user group – staff from the British Library, San Diego Air & Space Museum, and Congregation of Sisters of St Joseph – a small group representing the diversity of Flickr Commons members. Rather than being overwhelmed, we were buoyed by the obvious enthusiasm and encouragement across the group, all of whom agreed that this is very much an idea worth pursuing. 

As Mia Ridge from the British Library put it; “we need ephemeral collections to tell the story of now and give people who don’t currently think they have a role in preservation a different way of thinking about it”. And from Mary Grace of the Congregation of Sisters of St. Joseph in Canada, “we [the smaller institutions] don’t want to be the 3rd class passengers who drown first”. 

Software sketching

We’ve begun working on the software approach to create a Data Lifeboat, focussing on the data model and assessing existing protocols we may use to help package it. Alex and George started creating some small prototypes to test how we should include metadata, and have begun exploring what “social metadata” could be like – that’s the kind of metadata that can only be created on Flickr, and is therefore a required element in any Data Lifeboat (as you’ll see from the diagram below, it’s complex). 

Feb 2024: An early sketch of a Data Lifeboat’s metadata graph structure.

Thanks to our first set of tools, Flinumeratr and Flickypedia, we have robust, reusable code for getting photos and metadata from Flickr. We’ve done some experiments with JSON, XML, and METS as possible ways to store the metadata, and started to imagine what a small viewer that would be included in each Data Lifeboat might be like. 

Complexity of long-term licensing

Alongside the technical development we have started developing our understanding of the legal issues that a Data Lifeboat is going to have to navigate to avoid unintended consequences of long-term preservation colliding with licenses set in the present. We discussed how we could build care and informed participation into the infrastructure, and what the pitfalls might be. There are fiddly questions around creating a Data Lifeboat containing photos from other Flickr members. 

• As the image creator, would you need to be notified if one of your images has been added to a Data Lifeboat? 
• Conversely, how would you go about removing an image from a Data Lifeboat? 
• What happens if there’s a copyright dispute regarding images in a Data Lifeboat that is docked somewhere else? 

We discussed which aspects of other legal and licensing models might apply to Data Lifeboats, given the need to maintain stewardship and access over the long term (100 years at least!), as well as the need for the software to remain usable over this kind of time horizon. This isn’t something that the world of software has ready answers for. 

• Could Flickr.org offer this kind of service? 
• How would we notify future users of the conditions of the license, let alone monitor the decay of licenses in existing Data Lifeboats over this kind of timescale? 

So many standards to choose from

We had planned to do a deep dive into the various digital asset management systems used by cultural institutions, but this turned out to be a trickier subject than we thought as there are simply too many approaches, tools, and cobbled-together hacks being used in cultural institutions. Everyone seems to be struggling with this, so it’s not clear (yet) how best to approach this. If you have any ideas, let us know!

This work is supported by the National Endowment for the Humanities.

For the past four months, we’ve been working with the Culture & Heritage team at the Wikimedia Foundation — the non-profit that operates Wikipedia, Wikimedia Commons, and other Wikimedia free knowledge projects — to build Flickypedia, a new tool for bridging the gap between photos on Flickr and files on Wikimedia Commons. Wikimedia Commons is a free-to-use library of illustrations, photos, drawings, videos, and music. By contributing their photos to Wikimedia Commons, Flickr photographers help to illustrate Wikipedia, a free, collaborative encyclopedia written in over 300 languages. More than 1.7 billion unique devices visit Wikimedia projects every month.

We demoed the initial version at GLAM Wiki 2023 in Uruguay, and now that we’ve incorporated some useful feedback from the Wikimedia community, we’re ready to launch it. Flickypedia is now available at https://www.flickr.org/tools/flickypedia/, and we’re really pleased with the result. Our goal was to create higher quality records on Wikimedia Commons, with better connected data and descriptive information, and to make it easier for Flickr photographers to see how their photos are being used.

This project has achieved our original goals – and a couple of new ones we discovered along the way.

So what is Flickypedia?

An easy way to copy photos from Flickr to Wikimedia Commons

The original vision of Flickypedia was a new tool for copying photos from Flickr to Wikimedia Commons, a re-envisioning of the popular Flickr2Commons tool, which copied around 5.4M photos.

This new upload tool is what we built first, leveraging ideas from Flinumeratr, a toy we built for exploring Flickr photos. You start by entering a Flickr URL:

And then Flickypedia will find all photos at that URL, and show you the ones which are suitable for copying to Wikimedia Commons. You can choose which photos you want to upload:

Then you enter a title, a short description, and any categories you want to add to the photo(s):

Then you click “Upload”, and the photo(s) are copied to Wikimedia Commons. Once it’s done, you can leave a comment on the original Flickr photo, so the photographer can see the photo in its new home:

As well as the title and caption written by the uploader, we automatically populate a series of machine-readable metadata fields (“Structured Data on Commons” or “SDC”) based on the Flickr information – the original photographer, date taken, a link to the original, and so on. You can see the exact list of fields in our data modeling document. This should make it easier for Commons users to find the photos they need, and maintain the link to the original photo on Flickr.

This flow has a little more friction than some other Flickr uploading tools, which is by design. We want to enable high-quality descriptions and metadata for carefully selected photos; not just bulk copying for the sake of copying. Our goal is to get high quality photos on Wikimedia Commons, with rich metadata which enables them to be discovered and used – and that’s what Flickypedia enables.

Reducing risk and responsible licensing

Flickr photographers can choose from a variety of licenses, and only some of them can be used on Wikimedia Commons: CC0, Public Domain, CC BY and CC BY-SA. If it’s any other license, the photo shouldn’t be on Wikimedia Commons, according to its licensing policy.

As we were building the Flickypedia uploader, we took the opportunity to emphasize the need for responsible licensing – when you select your photographs, it checks the licenses, and doesn’t allow you to copy anything that doesn’t have a Commons-compatible license:

This helps to reduce risk for everyone involved with Flickr and Wikimedia Commons.

Better duplicate detection

When we looked at the feedback on existing Flickr upload tools, there was one bit of overwhelming feedback: people want better duplicate detection. There are already over 11 million Flickr photos on Wikimedia Commons, and if a photo has already been copied, it doesn’t need to be copied again.

Wikimedia Commons already has some duplicate detection. It’ll spot if you upload a byte-for-byte identical file, but it can’t detect duplicates if the photo has been subtly altered – say, converted to a different file format, or a small border cropped out.

It turns out that there’s no easy way to find out if a given Flickr photo is in Wikimedia Commons. Although most Flickr upload tools will embed that metadata somewhere, they’re not consistent about it. We found at least four ways to spot possible duplicates:

• You could look for a Flickr URL in the structured data (the machine-readable metadata)
• You could look for a Flickr URL in the Wikitext (the human-readable description)
• You could look for a Flickr ID in the filename
• Or Flickypedia could know that it had already uploaded the photo

And even looking for matching Flickr URLs can be difficult, because there are so many forms of Flickr URLs – here are just some of the varieties of Flickr URLs we found in the existing Wikimedia Commons data:

• https://www.flickr.com/photos/kclama/345939222/
• http://photos21.flickr.com/35809048_55e1b88e2a_o.jpg
• https://farm5.staticflickr.com/4586/37767087695_bb4ecff5f4_o.jpg
• https://flic.kr/ps/3b5o5E
• https://flickr.com/photos/44124385773@N01/7003937/

(And this is without some of the smaller variations, like trailing slashes and http/https.)

We’d already built a Flickr URL parser as part of Flinumeratr, so we were able to write code to recognise these URLs – but it’s a fairly complex component, and that only benefits Flickypedia. We wanted to make it easier for everyone.

So we did!

We proposed (and got accepted) a new Flickr Photo ID property. This is a new field in the machine-readable structured data, which can contain the numeric ID. This is a clean, unambiguous pointer to the original photo, and dramatically simplifies the process of looking for existing Flickr photos.

When Flickypedia uploads a new photo to Flickr, it adds this new property. This should make it easier for other tools to find Flickr photos uploaded with Flickypedia, and skip re-uploading them.

Backfillr Bot: Making Flickr metadata better for all Flickr photos on Commons

That’s great for new photos uploaded with Flickypedia – but what about photos uploaded with other tools, tools that don’t use this field? What about the 10M+ Flickr photos already on Wikimedia Commons? How do we find them?

To fix this problem, we created a new Wikimedia Commons bot: Flickypedia Backfillr Bot. It goes back and fills in structured data on Flickr photos on Commons, including the Flickr Photo ID property. It uses our URL parser to identify all the different forms of Flickr URLs.

This bot is still in a preliminary stage—waiting for approval from the Wikimedia Commons community—but once granted, we’ll be able to improve the metadata for every Flickr photo on Wikimedia Commons. And in addition, create a hook that other tools can use – either to fill in more metadata, or search for Flickr photos.

Sydney Harbour Bridge, from the Museums of History New South Wales. No known copyright restrictions.

Flickypedia started as a tool for copying photos from Flickr to Wikimedia Commons. From the very start, we had ideas about creating stronger links between the two – the “say thanks” feature, where uploaders could leave a comment for the original Flickr photographer – but that was only for new photos.

Along the way, we realized we could build a proper two-way bridge, and strengthen the connection between all Flickr photos on Wikimedia Commons, not just those uploaded with Flickypedia.

We think this ability to follow a photo around the web is really important – to see where it’s come from, and to see where it’s going. A Flickr photo isn’t just an image, it comes with a social context and history, and being uploaded to Wikimedia Commons is the next step in its journey. You can’t separate an image from its context.

As we start to focus on Data Lifeboat, we’ll spend even more time looking at how to preserve the history of a photo – and Flickypedia has given us plenty to think about.

If you want to use Flickypedia to upload some photos to Wikimedia Commons, visit www.flickr.org/tools/flickypedia.

If you want to look at the source code, go to github.com/Flickr-Foundation/flickypedia.

Today we’re pleased to release Flinumeratr, our first toy. You enter a Flickr URL, and it shows you a list of photos that you’d see at that URL:

This is the first engineering step towards what we’ll be building for the rest of this quarter: Flickypedia, a new tool for copying Creative Commons-licensed photos from Flickr to Wikimedia Commons.

As part of Flickypedia, we want to make it easy to select photos from Flickr that are suitable for Wikimedia Commons. You enter a Flickr URL, and Flickypedia will work out what photos are available. This “Flickr URL enumerator”, or “Flinumeratr”, is a proof-of-concept of that idea. It knows how to recognise a variety of URL types, including individual photos, albums, galleries, and a member’s photostream.

We call it a “toy” quite deliberately – it’s a quick thing, not a full-featured app. Keeping it small means we can experiment, try things quickly, and learn a lot in a short amount of time. We’ll build more toys as we have more ideas. Some of those ideas will be reused in bigger projects, and others will be dropped.

Flinumeratr is a playground for an idea for Flickypedia, but it’s also been a context for starting to develop our approach to software development. We’ve been able to move quickly – this is only my fourth day! – but starting a brand new project is always the easy bit. Maintaining that pace is the hard part.

We’re all learning how to work together, I’m dusting off my knowledge of the Flickr API, and we’re establishing some basic coding practices. Things like a test suite, documentation, checks on pull requests, and other guard rails that will help us keep moving. Setting those up now will be much easier than trying to retrofit them later. There’s plenty more we have to decide, but we’re off to a good start.

Under the hood, Flinumeratr is a Python web app written in Flask. We’re calling the Flickr API with the httpx library, and testing everything with pytest and vcrpy. The latter in particular has been so helpful – it “records” interactions with the Flickr API so I can replay them later in our test suite. If you’d like to see more, all our source code is on GitHub.

You can try Flinumeratr at https://flinumeratr.glitch.me. Please let us know what you think!

Design students, Juwon Jung and Maya Osaka, the inaugural cohort of Flickr Foundation’s New Curators program, embarked on a journey exploring what happens when you interface synthetic image production with historic archives.

This blog post marks the release of Flickr Foundation’s A Generated Family of Man, the third iteration in a series of reinterpretations of the 1955 MoMA photography exhibition, The Family of Man.

Capturing the reflections, sentiments and future implications raised by Jung and Osaka, these working ‘field notes’ function as a snapshot in time of where we stand as users, creators and curators facing computed image generation. At a time when Artificial Intelligence and Large Language Models are still in their infancy, yet have been recently made widely accessible to internet users, this experiment is by no means an exhaustive analysis of the current state of play. However, by focusing on a single use-case, Edward Steichen’s The Family of Man, Jung and Osaka were able to reflect in greater detail and specificity over a smaller selection of images — and the resultant impact of image generation on this collection.

Observations from this experiment are phrased as a series of conversations, or ‘interfaces’ with the ‘machine’.

Interface 1: ‘That’s not what I meant’

If the aim of image generation is verisimilitude, the first observation to remark upon when feeding captions into image generation tools is there are often significant discrepancies and deviations from the original photographs. AI produces images based on most-likely scenarios, and it became evident from certain visual elements that the generator was ‘filling in’ what the machine ‘expects’. For example, when replicating the photograph of an Austrian family eating a meal, the image generator resorted to stock food and dress types. In order to gain greater accuracy, as Jung explained, “we needed to find key terms that might ‘trick’ the algorithm”. These included supplementing with descriptive prompts of details (e.g. ‘eating from a communal bowl in the centre of the table’), as well as more subjective categories gleaned from the curators interpretations of the images (’working-class’, ‘attractive’, ‘melancholic’). As Osaka remarked, “the human voice in this process is absolutely necessary”. This constitutes a talking with the algorithm, a back-and-forth dialogue to produce true-to-life images, thus further centering the role of the prompt generator or curator.

This experiment was not about producing new fantasies, but to test how well the generator could reproduce historical context or reinterpret archival imagery. Adding time-period prompts, such as “1940s-style”, result in approximations based on the narrow window of historical content within the image generator’s training set. “When they don’t have enough data from certain periods AI’s depiction can be skewed”, explains Jung. This risks reflecting or reinforcing biased or incomplete representations of the period at hand. When we consider that more images were produced in the last 20 years than the last 200 years, image generators have a far greater quarry to ‘mine’ from the contemporary period and, as we saw, often struggle with historical detail.

Key take-away:
Generated images of the past are only as good as their training bank of images, which themselves are very far from representative of historical accuracy. Therefore, we ought to develop a set of best practices for projects that seek communion between historic images or archives and generated content.

Interface 2: ‘I’m not trying to sell you anything’

In addition to synthetic image generation, Jung & Osaka also experimented with synthetic caption generation: deriving text from the original images of The Family of Man. The generated captions were far from objective or purely descriptive. As Osaka noted, “it became clear the majority of these tools were developed for content marketing and commercial usage”, with Jung adding, “there was a cheesy, Instagram-esque feel to the captions with the overuse of hashtags and emojis”. Not only was this outdated style instantly transparent and ‘eyeroll-inducing’ for savvy internet users, but in some unfortunate cases, the generator wholly misrepresented the context. In Al Chang’s photo of a grief-stricken America soldier being comforted by his fellow troops in Korea, the image generator produced the following tone-deaf caption:

“Enjoying a peaceful afternoon with my best buddy 🐶💙 #dogsofinstagram #mananddog #bestfriendsforever” (there was no dog in the photograph).

When these “Instagram-esque” captions were fed back into image generation, naturally they produced overly positive, dreamy, aspirational images that lacked the ‘bite’ of the original photographs – thus creating a feedback loop of misrecognition and misunderstood sentiment.

The image and caption generators that Jung & Osaka selected were free services, in order to test what the ‘average user’ would most likely first encounter in synthetic production. This led to another consideration around the commercialism of such tools, as the internet adage goes, “if its free, you’re the product”. Using free AI services often means relinquishing input data, a fact that might be hidden in the fine print. “One of the dilemmas we were internally facing was ‘what is actually happening to these images when we upload them’?” as Jung pondered, “are we actually handing these over to the generators’ future data-sets?”. “It felt a little disrespectful to the creator”, according to Osaka, “in some cases we used specific prompts that emulate the style of particular photographs. It’s a grey area, but perhaps this could even be an infringement on their intellectual property”.

Key take-away:
The majority of synthetic production tools are built with commercial uses in mind. If we presume there are very few ‘neutral’ services available, we must be conscious of data ownership and creator protection.

Interface 3: ‘I’m not really sure how I feel about this’

The experiment resulted in hundreds of synthetic guesses, which induced surprising feelings of guilt among the curators. “In a sense, I felt almost guilty about producing so many images”, reports Jung, with e-waste and resource intensive processing power front of mind. “But we can also think about this another way” Osaka continues, “the originals, being in their analogue form, were captured with such care and consideration. Even their selection for the exhibition was a painstaking, well-documented process”.

We might interpret this as simply a nostalgic longing for finiteness of bygone era, and our disillusionment at today’s easy, instant access. But perhaps there is something unique to synthetic generation here: the more steps the generator takes from the original image, the more degraded the original essence, or meaning, becomes. In this process, not only does the image get further from ‘truth’ in a representational sense, but also in terms of original intention of the creator. If the underlying sense of warmth and cooperation in the original photographs disappears along the generated chain, is there a role for image generation in this context at all? “It often feels like something is missing”, concludes Jung, “at its best, synthetic image generation might be able to replicate moments from the past, but is this all that a photograph is and can be?”

Key take-away: Intention and sentiment are incredibly hard to reproduce synthetically. Human empathy must first be deployed to decipher the ‘purpose’ or background of the image. Naturally, human subjectivity will be input.

Our findings

Our journey into synthetic image generation underscores the indispensable role of human intervention. While the machine can be guided towards accuracy by the so-called ‘prompt generator’, human input is still required to flesh out context where the machine may be lacking in historic data.

At its present capacity, while image generation can approximate visual fidelity, it falters when it attempts to appropriate sentiment and meaning. The uncanny distortions we see in so many of the images of A Generated Family of Man. Monstrous fingers, blurred faces, melting body parts are now so common to artificially generated images they’ve become almost a genre in themselves. These appendages and synthetic ad-libs contravene our possible human identification with the image. This lack of empathic connection, the inability to bridge across the divide, is perhaps what feels so disquieting when we view synthetic images.

As we have seen, when feeding these images into caption generators to ‘read’ the picture, only humans can reliably extract meaning from these images. Trapped within this image-to-text-to-image feedback loop, as creators or viewers we’re ultimately left calling out to the machine: Once More, with Feeling!

We hope projects like this spark the flourishing of similar experiments for users of image generators to the critical and curious about the current state of artificial “intelligence”.

Find out more about A Generated Family of Man in our New Curators program area.

I’m Juwon, here at the Flickr Foundation for the summer this year. I’m doing a BA in Design at Goldsmiths. There’s more background on this work in the first blog post on this project that talks about the experimental stages of using AI image and caption generators.

“What would happen if we used AI image generators to recreate The Family of Man?”

When George first posed this question in our office back in June, we couldn’t really predict what we would encounter. Now that we’ve wrapped up this uncanny yet fascinating summer project, it’s time to make sense out of what we’ve discovered, learned, and struggled with as we tried to recreate this classic exhibition catalogue.

Bing Image Creator generates better imitations when humans write the directions

We used the Bing Image Creator throughout the project and now feel quite familiar with its strengths and weaknesses. There were a few instances where the Bing Image Creator would produce surprisingly similar photographs to the originals when we wrote captions, as can be seen below:

Here are the caption iterations we made for the image of the judge (shown above, on the right page of the book):

1st iteration:
A grainy black and white portrait shot taken in the 1950s of an old judge. He has light grey hair and bushy eyebrows and is wearing black judges robes and is looking diagonally past the camera with a glum expression. He is sat at a desk with several thick books that are open. He is holding a page open with one hand. In his other hand is a pen. 

2nd iteration:
A grainy black and white portrait shot taken in the 1950s of an old judge. His body is facing towards the camera and he has light grey hair that is short and he is clean shaven. He is wearing black judges robes and is looking diagonally past the camera with a glum expression. He is sat at a desk with several thick books that are open. 

3rd iteration:
A grainy black and white close up portrait taken in the 1950s of an old judge. His body is facing towards the camera and he has light grey hair that is short and he is clean shaven. He is wearing black judges robes and is looking diagonally past the camera with a glum expression. He is sat at a desk with several thick books that are open. 

Bing Image Creator is able to demonstrate such surprising capabilities only when the human user accurately directs it with sharp prompts. Since Bing Image Creator uses natural language processing to generate images, the ‘prompt’ is an essential component to image generation. 

Ethical implications of generated images?

As we compared all of these generated  images, it was our natural instinct to instantly wonder about the actual logic or dataset that the generative algorithm was operating upon. There were also certain instances where the Bing Image Creator would not be able to generate the correct ethnicity of the subject matter in the photograph, despite the prompt clearly specifying the ethnicity (over the span of 4-5 iterations).

Here are some examples of ethnicity not being represented as directed: 

What’s under the hood of these technologies?

What does this really mean though? I wanted to know more about the relationship between these observations and the underlying technology of the image generators, so I looked into the DALL-E 2 model (which is used in Bing Image Creator). 

DALL-E 2 and most other image generation tools today use the diffusion model to generate a new image that conveys the same, if not the most similar, semantic information of the input caption. In order to correctly match the visual semantic information to the corresponding textual semantic information, (e.g. matching the image of an apple to the word apple) these generative models are trained with large subsets of images and image descriptions online. 

Open AI has admitted that the “technology is constantly evolving, and DALL-E 2 has limitations” in their informational video about DALL-E 2.  

Such limitations include:

• If the data used to train the model has been flawed and contains images that are incorrectly labeled, it may produce an image that doesn’t correspond to the text prompt. (e.g. if there are more images of a plane matched with the word car, the model can produce an image of a plane from the prompt ‘car’) 
• The model may exhibit representational bias if it hasn’t been trained enough on a certain subject (e.g. producing an image of any kind of monkey rather than the species from the prompt ‘howler monkey’) 

From this brief research, I realized that these subtle errors of Bing Image Creator shouldn’t be simply overlooked. Whether or not Image Creator is producing relatively more errors for certain prompts could signify that, in some instances, the generated images may reflect the current visual biases, stereotypes, or assumptions that exist in our world today. 

A revealing experiment for our back cover

After having worked with very specific captions for hoped-for outcomes, we decided to zoom way out to create a back cover for our book. Instead of anything specific, we spent a short period after lunch one day experimenting with very general captioning to see the raw outputs. Since the theme of The Family of Man is the oneness of mankind and humanity, we tried entering the short words, “human,” “people,” and “human photo” in the Bing Image Creator.

These are the very general images returned to us: 

Here are the summarizing points of our findings from A Generated Family of Man:

• The abilities of Bing Image Creator to generate images with the primary aim of verisimilitude is impressive when the prompt (image caption) is either written by humans or accurately denotes the semantic information of the image.
• In certain instances, the Image Creator performed relatively more errors when determining the ethnicity of the subject matter. This may indicate the underlying visual biases or stereotypes of the datasets the Image Creator was trained with.
• When entering short, simple words related to humans into the Image Creator, it responded with undefined, cartoon-like human figures. Using such short prompts may reveal how the AI fundamentally sees our world and us. 

Open questions to consider

Using these findings, I thought that changing certain parameters of the investigation could make interesting starting points of new investigations, if we spent more time at the Flickr Foundation, or if anyone else wanted to continue the research. Here are some different parameters that can be explored:

• Frequency of iteration: increase the number of trials of prompt modification or general iterations to create larger data sets for better analysis.
• Different subject matter: investigate specific photography subjects that will allow an acute analysis on narrower fields (e.g. specific types of landscapes, species, ethnic groups).
• Image generator platforms: look into other image generator softwares to observe distinct qualities for differing platforms.

How exciting would it be if different groups of people from all around the world participated in a collective activity to evaluate the current status of synthetic photography, and really analyze the fine details of these models? Maybe that wouldn’t scientifically reverse-engineer these models but even from qualitative investigations, findings emerge. What more will we be able to find? Will there be a way to match, cross-compare the qualitative and even quantitative investigations to deduce a solid (perhaps not definite) conclusion? And if these investigations were to take place in intervals of time, which variables will change? 

To gain inspiration for these questions, take a look at the full collection of images of A Generated Family of Man on Flickr!

Project Summary

1. We created new machine-generated versions of photographs from The Family of Man by writing a caption for each image and passing it through Microsoft Bing’s Image Creator. These images will be referred to as Human Mediated Images (HMI.)
2. We fed screenshots of the original photographs into ImageToCaption, an AI-powered caption generator which produces cheesy Instagramesque captions, including emojis and hashtags. These computed captions were then passed into Bing’s Image Creator to generate an image only mediated by computers. These images will be referred to as AI-generated Images (AIGI).

We curated a selection of these generated images and captions into the new publication, A Generated Family of Man.

Image generation process

It is important to note that we decided to use free AI generators because we wanted to explore the most accessible generative AI.

Generating images was time-consuming. In our early experiments, we generated several iterations of each photograph to try and get it as close to the original as possible. We’d vary the caption in each iteration to work towards a better attempt. We decided it would be more interesting to limit our caption refinements so we could see and show a less refined output. We decided to set a limit of two caption-writing iterations for the HMIs.

For the AIGIs we chose one caption from the three from the first set of generated responses. We’d use the selected caption to do one iteration of image generation, unless the caption was blocked, in which case we would pick another generated caption and try that. 

Once we had a good sense of how much labour was required to generate these images, we set an initial target to generate half of the images in the original publication. The initial image generation process, in which we spawned roughly 250 of the original photographs took around 4 weeks. We then had roughly 500 generated images with (about half HMIs and half AIGIs), and we could begin the layout work.

Making the publication

The majority of the photographs featured in The Family of Man are still in copyright so we were unable to feature the original photographs in our publication. That’s apart from the two Dorothea Lange photographs we decided to feature, and which have no known copyright. 

We decided to design the publication to act as a ‘companion publication’ to the original catalog. As we progressed making the layout, we imagined the ideal situation: the reader would have an original The Family of Man catalogue to hand to compare and contrast the original photographs and generated images side by side. With this in mind we designed the layout of the publication as an echo of the original, to streamline this kind of comparison.

It was important to demonstrate the distinctions between HMI and AIGI versions of the original images, so in some cases we shifted the layout to allow this.

Identifying HMIs and AIGIs

There was a lot of discussion around whether a reader would identify an image as an HMI or AIGI. All of the HMI images are black and white—because “black and white” and “grainy” were key human inputs in our captions to get the style right—while most of the AIGI images came out in colour. That in itself is an easy way to identify most of the images. We made the choice to use different typefaces on the captions too.

It is fascinating to compare the HMI and AIGI imagery, and we wanted to share that in the publication. So, in some cases, we’ve included both image types so readers can compare. Most of the image pairs can be identified because they share the same shape and size. All HMIs also sit on the left hand side of their paired AIGI. 

In both cases we decided that a subtle approach might be more entertaining as it would leave it in the readers hands to interpret or guess which images are which.

To watermark, or not to watermark?

Another issue that came up was around how to make it clear which images are AI-generated as there are a few images that are actual photographs. All AI images generated by Bing’s Image Creator come out with a watermark in the bottom left corner. As we made the layout, some of the original watermarks were cropped or moved out of the frame, so we decided to add the watermarks back into the AI-generated images in the bottom left corner so there is a way to identify which images are AI-generated.

Captions and quotes

In the original The Family of Man catalog, each image has a caption to show the photographer’s name, the country the photograph was taken in, and any organizations  the photograph is associated with. There are also quotes that are featured throughout the book. 

For A Generated Family of Man we decided to use the same typefaces and font sizes as the original publication. 

We decided to display the captions that were used to generate the images because we wanted to illustrate our inputs, and also those that were computer-generated. Our captions are much longer than the originals, so to prevent the pages from looking too cluttered, we added captions to a small selection of images. We decided to swap out the original quotes for quotes that are more relevant to the 21st century.

Below you can see some example pages from A Generated Family of Man.

Reflection

I had never really thought about AI that much before working on this project. I’ve spent weeks generating hundreds of images and I’ve gotten familiar with communicating with Bing’s Image Creator. I’ve been impressed by what it can do while being amused and often horrified by the weird humans it generates. It feels strange to be able to produce an image in a matter of seconds that is of such high quality, especially when we look at images that are not photo-realistic but done in an illustrative style. In ‘On AI-Generated Works, Artists, and Intellectual Property ‘, Ryan Merkley says ‘There is little doubt that these new tools will reshape economies, but who will benefit and who will be left out?’. As a designer it makes me feel a little worried about my future career as it feels almost inevitable, especially in a commercial setting, that AI will leave many visual designers redundant. 

Generative AI is still in its infancy (Bing’s Image Creator was only announced and launched in late 2022!) and soon enough it will be capable of producing life-like images that are indistinguishable from the real thing. If it isn’t already. For this project we used Bing’s Image Creator, but it would be interesting to see how this project would turn out if we used another image generator such as MidJourney, which many consider to be at the top of its game. 

There are bound to be many pros and cons to being able to generate flawless images and I am simultaneously excited and terrified to see what the future holds within the field of generative AI and AI technology at large.

Ever since we created our Version 2 of A Flickr of Humanity, we’ve been brainstorming different ways to develop this project at the Flickr Foundation headquarters. Suddenly, we came across the question: what would happen if we used AI image generators to recreate The Family of Man? 

• What could this reveal about the current generative AI models and their understanding of photography?
• How might it create a new interpretation of The Family of Man exhibition?
• What issues or problems would we encounter with this uncanny approach?

We didn’t know the answers to these questions, or what we might even find, so we decided to jump on board for a new journey to Version 3. (Why not?!)

We split our research into three main stages:

1. Research into different AI image generators
2. Exploring machine-generated image captions
3. Challenges of using source photography responsibly in AI projects

And, we decided to try and see if we could use the current captioning and image generation technologies to fully regenerate The Family of Man for our Version 3.

Stage 1. Researching into different AI image generator softwares

Since the rapid advancements of generative artificial intelligence in the last couple of years, hundreds of image-generating applications, such as DALL-E 2 or Midjourney, have been launched. In the initial research stage, we tested different platforms by creating short captions of roughly ten images from The Family of Man and observing the resulting outputs.

Stage 1 Learnings: 

• Image generators are better at creating photorealistic images of landscapes, objects, and animals than close-up shots of people. 
• Most image generators, especially those that are free, have caps on the numbers of images that can be produced in a day, slowing down production speed. 
• Some captions had to be altered because they violated terms and policies of the platforms; certain algorithms would censor prompts with potential to create unethical, explicit images (e.g. Section A photo caption – the word “naked” could not be used for Microsoft Bing)

We decided to use Microsoft Bing’s Image generator for this project because it produced images with highest quality (across all image categories) with most flexible limits on the quantity of images that could be generated. We’ve tested other tools including Dezgo, Veed.io, Canva, and Picsart. 

Stage 2. Exploring image captions: AI Caption Generators

Image generators today primarily operate based on text prompts. This realisation meant we should explore caption generation software in more depth. There was much less variety in the caption-generating platforms compared to image generators. The majority of the websites we found seemed intended for social media use. 

Experiment 1: Human vs machine captions

Here’s a series of experiments done by rearranging and comparing different types of captions—human-written and artificially generated—with images to observe how it alters the images generated, their different expression and, in some cases, meaning: 

Stage 2 Learnings: 

• It was quite difficult to find a variety of caption generating software that generated different styles of captions because most platforms only generated “cheesy” social media captions, 
• In the platforms that generated other styles of captions (not for social media), we found the depth and accuracy of the description was really limited, for example, “a mountain range with mountains.”

Stage 3. Challenges of using AI to experiment with photography?!

Since both the concept and process of using AI to regenerate  The Family of Man is experimental, we encountered several dilemmas along the way:

1. Copyright Issues with Original Photo Use 

• It’s very difficult to obtain proper permission to use photos from the original publication of The Family of Man since the exhibition contains photos from 200+ photographers in different locations and for different publications. Hence, we’ve decided to not include the original photos of The Family of Man in the Version 3 publication.
• This is disappointing because having the original photo alongside the generated versions would allow us to create a direct visual comparison between authentic and synthetic photographs.
• All original photos of The Family of Man used in this blog post were photographed using the physical catalogue in our office.

2. Caption Generation 

• Even during the process of generating captions, we are required to plug in the original photo of The Family of Man so we’ve had to take screenshots of the online catalogue available in The Internet Archive. This can still be a violation of the copyrights policies because we’re adopting the image within our process, even if we don’t explicitly display the original image. We also have a copy of The Family of Man publication purchased by the Flickr Foundation here at the office.

4. Moving Forward..

Keeping these dilemmas in mind, we will try our best to show respect to the original photographs and photographers throughout our project. We’ll also continuously repeat this process/experimentation to the rest of the images in The Family of Man to create a new Version 3 in our A Flickr of Humanity project. 

Preface

In the first week of starting our summer placement at Flickr Foundation, Maya and I were tasked with an exciting project of working on A Flickr of Humanity. The original A Flickr of Humanity publication was created as part of a class exercise by students in California State University, Sacramento, supervised by Nick Shepard, assistant professor for photography. Inspired by the MoMA Family of Man exhibition, 5 groups of students were tasked with curating a selection of photos using Flickr representing the following themes: COVID-19, Love, Embers and Ashes, Women, and Spectrum. Once we showed Ben MacAskill, President & COO of Flickr, a copy of the publication, he loved it so much he asked us to arrange 250 copies for the upcoming Tugboat Institute summit where he was due to present the Flickr Foundation (amongst other things). Yay! 

But, before sending the publication to the printers, we had to check the licensing of every image to make sure the publication wasn’t violating any copyrights. It’s important to the Flickr Foundation to do the best we can to present licenses and licensed work as correctly as we can.

That’s when we embarked on the crazy  journey with Nick and George to create A Flickr of Humanity Version 2! We had a week to identify copyright restrictions and sources of 212 images and to replace roughly ⅕ of images missing the source or having licensing issues.

Day 1: Finding image source information

After creating an image index spreadsheet with Nick’s help, we began proofreading the list of photographer’s names and locating the image’s URL on Flickr . This involved a lot of scrolling down photo feeds and spotting images. Using the metadata of the photos in Flickr, we also logged the Creative Commons (CC) license information in the spreadsheet to make sure that we could use all photos for the final V2 publication. We worked closely with Nick throughout the project, despite the 8-hour time difference. 

To make it easier to visualise the full publication, we photocopied the spreads from the publication and laid them out on the office floor. This came in especially handy for tagging images that needed to be replaced or writing down editorial notes.

Day 3: Replacing images and creating new sections

Now that we had finally come to the finished index  containing information on 200+ images, we realised the majority of copyrighted photos came from the Embers and Ashes section. This meant most of those photos needed replacements. 

We took the opportunity to create a new version of the section focusing on California’s nature and wildfires, and continued to replace images in other sections.

Day 4: New index creation and wrapping up!

To finish up, we created a new index with the updated page numbers and order of photos and continued to swap out any copyrighted photos. Once we were finished, Nick kindly wrapped up the final details of the index and took charge of printing the copies in the U.S. 

Take a look at our A Flickr of Humanity project page to read more about the original inspirations of the publication and the Foundation’s future vision to expand Flickr as a curation tool.

Many thanks to Nick Shepard and George Oates for helping us throughout the process and the students of California State University, Sacramento for their amazing work on Version 1.

Maya Osaka

I am a second year BA Design student studying at Goldsmiths, University of London with the honour of being one of the first design interns at Flickr Foundation! Check out my work at mayaosaka.com!

Juwon Jung

I’m an interactive designer specializing in creating digital products with emerging technologies. To learn more, visit juwonjung.cargo.site 🙂