Does anyone know where I can find some nice POC photography?
Im trying to make a character inspiration board but most of the photography is of pale skinned and skinny people.
@cutabello thatâs a great hashtag but they are mainly selfies and im looking for things like this:
Im trying to find POC photography where there are in the world and being magical, or being with family, or going on adventures or anything other than it just being a selfie or talking about racism. Yes racsim should be addressed but Iâd love to see more POC in surrealistic or domestic photography where there isnât an explanation for them being there they just are. Like this:
@openheartopenhead OH MY GOSH????
Within 5 mintues I found EXACTLY what I was looking for? This is amazing i canât even fit all the photos i found im floored!!
Every one! Check out the website it is really good help
After seeing this post I created a board for images PoC characters on Pinterest (still pinning poc in other boards but I thought it would also help to have a separate board so that people donât have to sort through all my other âStoryboard Resourcesâboards and sections) (Itâs not organized in any way just thought Iâd break it up into sections to make the searching seem more bearable as more and more images get pinned there)
today i learned that the finnish word for âhazardous wasteâ is ongelmajĂ€te, which can also translate as âproblematic garbageâ and my roommate and i immediately agreed this is a word that belongs on tumblr.
Text of tweet under the cut because it is loooong.
But... Stochastic Parrots.
Timnit Gebru was fired from Google in December 2020 for refusing to retract a research paper, and every single warning that paper made about large language models has now happened at a scale the industry spent 4 years trying to make people forget about.
Her name is Timnit Gebru.
She co-led the Ethical AI team at Google. She co-wrote a paper called "On the Dangers of Stochastic Parrots" with Emily Bender at the University of Washington and two other researchers. The paper was 14 pages long. It was submitted to a top AI ethics conference. And it was the reason Google decided that one of the most senior Black women in AI research could no longer work there.
The story Google told publicly was that she resigned. The story she told, confirmed by 2,695 of her colleagues in an open letter, was that she was fired by email while on vacation because she refused to either retract the paper or remove her name from it.
The paper had not even been published yet.
Here is what she actually wrote, and why every prediction inside it has now come true.
The first warning was about scale itself. Bender and Gebru argued that training ever-larger models on ever-larger scrapes of the internet would produce systems that appeared fluent but had no actual understanding of language. They called these systems stochastic parrots because they would repeat patterns from training data with statistical confidence and zero comprehension. The paper predicted that this apparent intelligence would fool both users and developers into trusting outputs that were structurally incapable of being reliable.
This was 2020. GPT-3 had just come out. The paper predicted the hallucination problem before anyone had a word for it.
The second warning was about bias amplification. The paper documented in detail that internet-scale training data contains systematic overrepresentation of dominant viewpoints and underrepresentation of marginalized ones. The models would not just absorb this bias. They would amplify it, because the optimization process rewards confident outputs, and confidence in language patterns tracks frequency in the training set.
The prediction was that hiring tools built on these models would discriminate against women. That healthcare triage tools would underperform on Black patients. That loan approval systems would entrench inequality while presenting their decisions as neutral algorithmic judgment.
Every one of those things has now been documented in deployment.
Amazon's hiring algorithm penalized resumes that contained the word "women" in any context. Healthcare risk scoring algorithms used by major US hospitals were found to systematically underestimate the medical needs of Black patients. Apple Card's credit algorithm gave wives credit lines 10x lower than their husbands for the same financial profile.
The third warning was about environmental cost. The paper calculated that training a single large language model produced emissions equivalent to the lifetime output of 5 cars. The prediction was that the race to scale would create an environmental footprint that would eventually rival entire industries.
In 2024, Google's emissions were up 48% from 2019, and the company explicitly blamed AI infrastructure. Microsoft's were up 29%, same reason. Both companies have now quietly abandoned the climate commitments they were publicly celebrating the year Gebru was fired.
The fourth warning was about documentation. The paper argued that the training datasets being assembled were too large for anyone to actually audit. Nobody at Google, OpenAI, Meta, or any other lab could tell you with confidence what was in the data their models were trained on. This was not a temporary problem to be solved later. It was a permanent feature of the approach.
In 2023, researchers discovered that the LAION-5B dataset, used to train Stable Diffusion and other major image models, contained thousands of images of child sexual abuse material. The companies that had trained on the dataset had no way of knowing. The paper predicted that category of failure 3 years before it was found.
The fifth warning was the one Google cared about most.
Bender and Gebru argued that the deployment of these systems would centralize linguistic and cultural power in the hands of the small number of companies that could afford to train them. The internet would become a place where the dominant voice was a statistical average of dominant voices, presented as a neutral assistant. Languages underrepresented in the training data would degrade over time as more web content was generated by these systems and fed back into the next training run.
This is now happening in real time. A 2024 study found that 57% of new web content in English is AI-generated or AI-assisted. Researchers studying low-resource languages have documented active degradation in translation quality, because the synthetic content fed back into training is itself worse in those languages.
The paper Google fired her for predicted the model collapse problem before model collapse had a name.
The mechanism behind why this all happened is the part of her work that nobody quotes.
Gebru's argument was not that AI is dangerous in some abstract sci-fi sense. Her argument was that AI is dangerous in a very specific structural sense. The technology was being built by a small group of researchers who shared similar backgrounds, worked at similar companies, and were rewarded for shipping products faster than competitors. The incentive structure made it impossible for safety, ethics, and bias concerns to slow anything down. Anyone inside the system who raised those concerns was either ignored, sidelined, or removed.
She was making that argument from inside Google.
Then Google proved her right by removing her.
The team Google had built to make sure their AI was safe was dismantled in 90 days because they did the job they had been hired to do. Margaret Mitchell, the other co-lead of the Ethical AI team, was fired two months after Gebru for searching through her own emails for evidence of how Gebru had been treated.
Gebru did not stop. She founded DAIR, the Distributed AI Research Institute, in 2021. The mission is to do AI research outside the control of the companies that have a financial interest in not hearing the answers.
Every prediction in the Stochastic Parrots paper has now been validated by deployment. Hallucinations are an industry-wide problem the largest labs cannot solve. Bias amplification has been documented in hiring, healthcare, lending, and criminal justice. Environmental costs are larger than entire small countries. Training data audits remain impossible. Model collapse is an active research crisis at every major lab.
The question worth sitting with is the one almost no one in the industry will say out loud.
Every researcher with the technical credibility to call out these problems watched what happened to her in December 2020 and made a calculation about their own career. The number of people willing to speak publicly about safety and ethics issues inside the major AI labs collapsed after that firing and has not recovered.
The researcher Google fired for warning about exactly what is now happening was right.
The company that fired her is now the second-largest deployer of the technology she warned about.
And the people inside that company who agree with her are not allowed to say so.
The problem with giving advice to angry and suffering people is that rather frequently the thing they need to know to improve their position is the last thing they want to hear and not something they have the capacity to internalize or accept
Unfortunate truths you can tell people that would help if they could hear what it means and not just what it sounds like
You were the victim, and it wasnât fair, but itâs over now. Nobody came to save you, and Iâm sorry, but itâs too late for anyone to go back and do it different.
Youâre suffering over something that cannot be resolved. Youâre allowed to feel angry, or outraged, or betrayed, but there will eventually come a time that you donât feel that so violently anymore, and youâre going to want to have something good left to go back to.
You canât make anyone love you the way you need to be loved. Thatâs how a lot of good things end. Not with a clear sign, something blocking the road that says âdo not proceedâ, just a splitting of the path thatâs still moving somewhat in the same direction.
You canât fix them. Nothing you can do will fix them. And if they fix themselves, they canât do it for you- they have to do it for themselves as well, because otherwise a day may come when theyâre alone, and as long as they live, they are their only true constant. So you can support, and you can encourage, but the hardest part is up to them. And sometimes they canât do it even with your help.
Sometimes letting go of someone feels like mourning at their funeral before theyâve died, and every time you see them after itâs like talking to a ghost that doesnât know itâs dead. Sometimes that happens. Youâll both still wake up tomorrow anyways.
I understand that youâre afraid, and that youâre afraid for good reasons. And I understand that being brave isnât as easy as just turning that fear off, and you would if you could in a heartbeat. But the thing is, as long as that fear is able to dictate your choices, it will have power over you. If you donât believe you can try to fight it, if you accept that it will always be in charge, you let the frightening thing stay present in your life. It will exist as long as you stay paralyzed. And that sounds cruel, but it isnât something anyone can fix for you.
The person you may let yourself become after experiencing the terrible thing may very well grow into a much bigger, much more terrible thing, and someday it will swallow the first terrible thing whole. And all that will be left is something far worse for someone else. And you will not be able to shrink it down by explaining where it came from, because terrible things that are dead and gone are never as terrible as terrible things that are alive right now in front of you.
No matter how much or how little I love you, I still do not have the ability to help you the way you need to be helped. I might be the helper you want, but I am not a helper you can get. If you are to be helped at all, you will need to accept that it will come from someone else.
If anyone goes out of their way to find this user and harass them, please know thatâs shitty behaviour and I will be deeply disappointed, but I think they really helped to underline number 8 in a way I wished Iâd known to consider of others years ago
If you read this whole thing and found yourself angry, if you thought to yourself âI know that, and it doesnât help. I know that, and Iâm still suffering. I know that, Iâve heard that, Iâve been told that before, over and over and over again, by people who arenât listening who donât understand, who donât get it, and Iâm still hurting, still tired, still in pain, still suffering, and this isnât something a handful of pithy words from some asshole who isnât here and present and walking in my shoes suffering what Iâm suffering from can fix. I know all of this and it changes nothingâ, I want you to know:
Yeah. That was me, too. I sat at the bottom of a miserable pit that I didnât even dig while a bunch of detached, emotionally unavailable jackasses who werenât helping even a little yelled all this down at me, like just saying it hard enough or making me hear it as though I wasnât already a hundred percent aware and still hurting anyways would magically solve all my problems and it didnât. Like I was some whiny little rat with a victim complex looking for the easy way out and not the survivor of something awful doing their goddamn best to keep going, scraping by on the skin of their fucking teeth.
Every single note on this list is something someone told me at the exact wrong time, that made me want to scream and cry and smash a goddamn brick over their head because âI already know that, you fucking asshole, and it doesnât change anything, so fucking help me or piss the hell off.â
Thatâs why I wrote the list.
Itâs everything I needed to know that I already knew, that only made me feel worse, and didnât help me improve anything at all even a little bit until I experienced the exact right circumstances that made them click the exact right way and allowed me to say it to myself and feel only a sense of, âokay yeah, I get it now.â
Itâs not something I would ever directly say to someone in a time of crisis, but itâs all stuff I learned and needed to learn while I was that person.
You get what I mean?
The difference between knowing and internalizing, the difference between hearing the pain is temporary when youâve broken a bone and KNOWING the pain is temporary after its healed, is that you KNOW, but youâre still not done experiencing the part that makes it true and real and meaningful.
Book:Â The Word for World: The Maps of Ursula K. Le Guin, Edited by So Mayer and Sarah Shin, AA Publications / Spiral House, London, 2025; Contributors: Federico Campagna, Theo Downes-Le Guin, Daniel Heath Justice, Bhanu Kapil, Canisia Lubrin, Una McCormack, David Naimon, Nisha Ramayya, Shoshone Collective, Standard Deviation, Marilyn Strathern
Exhibition: The Word for World, Curated by Sarah Shin, Designed by Standard Deviation, AA Gallery, London, October 10, 2025 â December 6, 2025, supported by the Graham Foundation for Advanced Studies in the Fine Arts
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