"Something I've learned while in law school is about the social construction of crime. I work in a legal clinic on wage theft cases, where employers have "improperly paid" workers by not paying, paying below min wage, withholding overtime, paid sick time, etc.
Most theft is wage theft. Meaning, the dollar value of stolen wages is greater than the value, each year, of all burglaries + robberies, shoplifting, auto theft, combined. Yet, wage theft is not a crime."
Below this tweet is an image comparing the cost of robbery, auto theft, burglary, larceny and wage theft in billions. Robbery only hits 0.34 billion, auto theft 3.8 billion, burglary 4.1 billion, larceny 5.3 billion, and wage theft accounts for greater than 19 billion dollars. The data is sourced from the FBI and EPF.
"If you steal $100 from your employer, you will get arrested. If you call the police because your paycheck is $100 light, the police will tell you to file a complaint with the AG, and the AG will settle the case for between $50 and $200.
(That's actually not true, because AG's only take on big cases where thousands of dollars are at stake, but they will settle big cases by typically requiring the employer to properly pay what is owed. No jail, no criminal record.)
If the AG doesn't want to take the case, it will give you a Private Right of Action to sue the employer in civil court for what you are owed, plus damages. It can take a 6 to 18 months to win at trial, and months or years to collect on the judgement if you win.
This is what we mean when we say crime is socially constructed. Not all social harms are criminalized. Not all actors committing social harm are criminalized.
I settled a case for $27k for three clients last year. We spent a MONTH negotiating the non-disclosure agreement because the employer stated if all his employees sued him and settled like this, he would go BANKRUPT. His business model DEPENDED on wage theft.
These employers go on to hold elected office. 45 famously used wage theft to improve his finances on construction projects, leaving a trail of victims in his wake. Some sued and he had to pay them. Others didn't have resources to pursue multi-year litigation + got nothing."
Then the user responds to someone else asking a question.
The question:
"Can you explain this reasoning? Why expanding criminal liability is a bad idea? For whom?"
The user replies:
"What should we do about it? Criminalize employers or decriminalize theft or something else?
Wage theft shows that we believe restitution is important. Giving the money back is important. Currently, AG keeps track of bad actors and will increase future penalties for bad actors.
It also shows when harm is committed, we don't have to lock someone in a cage or label them a felon, both of which destroy years of life even after the sentence is over. We can demand restitution instead of punishment.
It also shows how ridiculous the label "high crime neighborhood" is. And the arbitrary and racist response of police surveillance in HCN. Because we defined it that way.
Consider the social construction of murder:
The people committing the most harm aren't in jail, don't live in high crime neighborhoods. And "black people commit more crime" is true only because of how we have defined crimes, and how we then surveil their community in response to find more crimes.
There are so many orgs trying to address harm and create accountability within community + without incarceration. We call ourselves prison abolitionists.
Just a few: @ byp100, @ survivepunishNY, @ justicehealing, @ DeeperThanWater, @ BlackAndPinkBos and @ BlmBoston"
Women stick thin and malnourished on the red carpet, and people are saying you can't point out that these women are dying because that's body shaming. Girl.
ok well i filled up my car with gas and got cat food for my cats so idk how this applies to me also the “don’t buy coffee anymore” thing is rlly annoying from ppl acting like buying coffee is the reason ppl r struggling to keep purchases under 20 dollars instead of capitalism inflating prices for shareholders to buy another five houses like. eventually yall gotta stop doing the “no more avacado toast!” thing to ppl bc there is no budgeting that is enough to outrun inflation
Drugs are morally neutral. Doing or not doing drugs is not an indicator of how good a person is. There are addicts who’d give you the shirt off their back, and sober people who poison the homeless for fun.
[Video description: 2 toilets swinging from ropes. They have CRT computer monitors attached to the top. One toilet has a blue monitor and red water in the toilet bowl, the other has a red monitor and blue water. The toilets swing freely next to each other, having near misses, glancing blows, and direct hits until a collision of the backs of the monitors knocks the blue monitor off the toilet. After a cut the toilets are swinging more energetically, after a few more swings the toilet bowl with blue water smashes into the other toilet and breaks. End description.]
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.
He didn't steal 10 million dollars. They made that number up as a loss, they never fucking had it. Rockstar has spent more than a billion fucking dollars on GTA VI and will likely make billions more when it gets released.
Uber is a fucking shell game of a company designed to leech investor capital and output bootleg cabs.
Nvidia posted a profit in 2023 of $4.37 billion. This is like someone stealing less than a penny from me.
And they lock this kid in a prison hospital for LIFE?
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