alright I've got to do some quick math to explain attitudes towards AI to my boss.
we're looking to create an AI policy, and when we were talking about this, my boss (older millennial) was genuinely shocked to hear that younger people do not (seem) to view AI positively (a la the recent commencement speakers being booed)
please rb for larger sample size!
Question 1/3
What is your age, and do you feel AI is a net positive or net negative in our lives today?
Solidarity between LGBTQ+ people and unions has saved an event denied ‘a single penny’ of council money
"What’s the point of supporting gay rights but nobody else’s rights. You know? Or - workers’ rights but not Women’s rights - it’s - I don’t know - illogical."
"There’s a lodge banner down in the welfare. We bring it out for special occasions. It’s a hundred years old. I’ll show it to you one day. It’s a symbol like this -
Two hands.
That’s what the labour movement means. Should mean. You support me and I support you. Whoever you are. Wherever you come from. Shoulder to shoulder. Hand to hand."
@anthropologist-on-the-loose get peer-reviewed because your shared experience with the subject of the painting really heightened the emotional impact of this artwork for me ( An impact which was already high tbh. The idea that Pompeii was built by generations, buried by generations, uncovered by generations. What if I just started screaming and never stopped. )
But it was buried by generations! Yes, it was buried in a volcanic eruption, but it was also figuratively buried. Over the centuries the location of Pompeii was lost, and it was found again by accident during construction projects. The ruins were not conclusively identified as the city of Pompeii until the 18th century (more than a millennia and a half after the eruption!) and it has been excavated ever since. People have been digging there since before the formation of the United States.
“If a society puts half its children into short skirts and warns them not to move in ways that reveal their panties, while putting the other half into jeans and overalls and encouraging them to climb trees, play ball, and participate in other vigorous outdoor games; if later, during adolescence, the children who have been wearing trousers are urged to “eat like growing boys,” while the children in skirts are warned to watch their weight and not get fat; if the half in jeans runs around in sneakers or boots, while the half in skirts totters about on spike heels, then these two groups of people will be biologically as well as socially different. Their muscles will be different, as will their reflexes, posture, arms, legs and feet, hand-eye coordination, and so on. Similarly, people who spend eight hours a day in an office working at a typewriter or a visual display terminal will be biologically different from those who work on construction jobs. There is no way to sort the biological and social components that produce these differences. We cannot sort nature from nurture when we confront group differences in societies in which people from different races, classes, and sexes do not have equal access to resources and power, and therefore live in different environments. Sex-typed generalizations, such as that men are heavier, taller, or stronger than women, obscure the diversity among women and among men and the extensive overlaps between them… Most women and men fall within the same range of heights, weights, and strengths, three variables that depend a great deal on how we have grown up and live. We all know that first-generation Americans, on average, are taller than their immigrant parents and that men who do physical labor, on average, are stronger than male college professors. But we forget to look for the obvious reasons for differences when confronted with assertions like ‘Men are stronger than women.’ We should be asking: ‘Which men?’ and ‘What do they do?’ There may be biologically based average differences between women and men, but these are interwoven with a host of social differences from which we cannot disentangle them.”
— Ruth Hubbard, “The Political Nature of ‘Human Nature’“
when thinking about how oppression works, on a structural level, my guiding principle is that I must spend at least as much time looking down as I do looking up.
what do I mean by this? here's an example. when my surgery is delayed multiple times, I spend a little time looking up (there is only one surgeon in the entire area who will perform this surgery on trans people, so every trans person's surgical timeline is bottle-necked and delayed by months every time he goes to a conference or takes a vacation or experiences an injury. in other words, if I was cis, I would not encounter this difficulty in accessing surgery). and then I spend time looking down (due to nonstop harassment and legal threats, this practice now only treats adults and will no longer perform surgeries on minors. in other words, my access to surgery is predicated on adult privilege I have at the direct expense of trans youth's lack of access).
if you do not build a habit around thinking in this way, you will become the person Audre Lorde describes as "so enamored of her own oppression that she cannot see her heelprint upon another woman's face." If we are seeking to dismantle structures of oppression, rather than to simply use and climb them, then we absolutely must make a practice of looking in both directions, especially when we feel like we're on the bottom.
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.
Just a note that Timnit Gebru didn't go away and stop doing important work after being fired from Google! She founded the Distributed AI Research Institute (DAIR). Among other things, DAIR hosts the Luddite Lab Worker Resource Hub, which partners with unions to help workers push back on mandatory AI in the workplace. She's continuing the fight and actively resisting many of the things she and her coauthors warned us about in 2020, and maybe you can too.
Saw an op-ed that was on the surface a complaint about kids not wanting to take on family heirlooms but read like an elegy to dying traditions. The hardest part was the anxiety without recognizing that they didn’t pave the way for the decisions they assumed their kids would make.
(This is written entirely within the dominant white/western culture - about traditions that have neglectful stewardship rather than those actively suppressed)
The anxiety makes sense. You’re seeing, too late to do anything about it, that there’s no foundation - no space - for the traditions you expected to pass on. Your kids _can’t_ take your mom’s fine china. So now instead of enjoying what you have you worry about its future.
I see a pattern in these op-eds though - a pattern in what’s left unsaid. There were responsibilities tied to these traditions. You collectively assumed they _would_ be passed along. So collectively, what did you do to ensure those traditions _could_ be passed along?
Op-eds never speak for everyone, but it’s worth acknowledging the pattern in what speech is deemed worth sharing widely. And in this particular pattern, there’s an answer: that answer looks like “nothing.”
You want the china passed down but your kids have no room in their rentals. You want grandkids but your kids don’t have the financial stability. You want that cross-country RV neverending road trip but you’ve had decades of wanting lower taxes more than you wanted infrastructure.
The bleak outlook for traditions is a direct result of the unmaintained foundations for them. The second best time is always now - if it’s important enough to op-ed about, what are you willing to change to get it back? What will you give up or re-prioritize?
I kinda think that world-defining assumptions are always gonna break without maintenance. So rather than getting mad at whoever’s next for not carrying on the norms we didn’t do upkeep on, when it’s my turn, I hope I’m introspective enough to help instead of externalize & blame.
The bleak outlook for traditions is a direct result of the unmaintained foundations for them. The second best time is always now - if it’s important enough to op-ed about, what are you willing to change to get it back? What will you give up or re-prioritize?
I follow a Facebook group of “Memories of …” for my hometown - a rustbelt community that has gone from a thriving hub of industry to a much-less-thriving place.
The group is a collective lament. Decades-old pictures of well-kept churches. Aerial shots of the main intersection downtown, lined with big cars. Scanned advertisemetns from local stores featuring pictures of their interiors. These alternate with the drumbeat of news: the Catholic diocese is closing churches. Selling them. Tearing them down. STores downtown are closing. The traffic light has been replaced with a four-way-stop.
“That’s the church my parents were married in!”
“How could they tear down that beautiful building. Such memories!”
“All the businesses are closing. It must be the taxes.”
”They’ve sold the old lodge downtown.”
“They’re not opening the skating rink this year. We always used to go.”
And sometimes I chime in.
“Do you attend that church? Do you give? Or do you just want the building to look pretty for you? “
“Do you volunteer at that park? Why not?”
“Did you vote for that recreation bond issue?”
“Are you a member of that Lodge? Why not?”
“Do you shop downtown? Or did you start shopping at Walmart and Amazon to save a few bucks?”
If you feel something is worth preserving, why do you not participate in its preservation?
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