How convenient that a counterexample can’t be named

I feel like Luthor was a better counterexample for this before the model for his billionaire redesign was elected President of the USA.

Even so, Luthor hasn’t had quite the same volume of appearances as Iron Man, Batman, Captain America and the other rich superhero tropes.

People have grown up reading comic books and watching movies about generous billionaire superhero saviors. They want to believe that exists because it’s what they’ve been taught justice looks like.

If only all my snark could elicit such absurd perfection.

He’s still a party member, it’s listed in his candidate information sheet. Badly Scanned PDF

You’re both adorable

It can, it just doesn’t want to.

So long as you don’t care about whether they’re the right or relevant answers, you do you, I guess. Did you use AI to read the linked post too?

Joy isn’t reserved for the young, but it’s sure fucking easier to be joyful when your body hurts less because you’re far less likely to have one or more chronic pain conditions in your youth.

Your heart won’t harden? It might just with atherosclerosis and enough time.

So go enjoy the joy even more now while it’s still easier.

References weren’t paywalled, so I assume this is the paper in question:

Hofmann, V., Kalluri, P.R., Jurafsky, D. et al. AI generates covertly racist decisions about people based on their dialect. Nature (2024).

Abstract

Hundreds of millions of people now interact with language models, with uses ranging from help with writing^1,2 to informing hiring decisions³. However, these language models are known to perpetuate systematic racial prejudices, making their judgements biased in problematic ways about groups such as African Americans^4,5,6,7. Although previous research has focused on overt racism in language models, social scientists have argued that racism with a more subtle character has developed over time, particularly in the United States after the civil rights movement^8,9. It is unknown whether this covert racism manifests in language models. Here, we demonstrate that language models embody covert racism in the form of dialect prejudice, exhibiting raciolinguistic stereotypes about speakers of African American English (AAE) that are more negative than any human stereotypes about African Americans ever experimentally recorded. By contrast, the language models’ overt stereotypes about African Americans are more positive. Dialect prejudice has the potential for harmful consequences: language models are more likely to suggest that speakers of AAE be assigned less-prestigious jobs, be convicted of crimes and be sentenced to death. Finally, we show that current practices of alleviating racial bias in language models, such as human preference alignment, exacerbate the discrepancy between covert and overt stereotypes, by superficially obscuring the racism that language models maintain on a deeper level. Our findings have far-reaching implications for the fair and safe use of language technology.