41–50 of 124 posts
What is/was the source of this rather than random tumblr? This May 26th Twitter post ...maybe? Account now suspended https://x.com/heygurisingh/status/2059251382960734593 ( http://web.archive.org/web/20260526123243/https://twitter.co... )
(direct link: https://x.com/nikitabier/status/2059789636885790911 )
The warnings: > 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. > The second warning was about bias amplification. The paper documented in detail that internet-scale training data contains systematic overrepresentation of dominant viewpoints…
There has been plenty of research that shows LLMs encode social biases. It seems pretty obvious even before looking at the research that training on the whole internet will end up encoding widely-held social biases and stereotypes. https://arxiv.org/pdf/2508.07111 https://github.com/angl1n/social-bias-llm-vlm
At the risk of stepping into a hornets nest: is that different than "knowledge"?
Or maybe, what would it mean if an LLM had no social biases? (Would we ever agree that was the case?)
Earlier quoted context omitted.
If the criticism can't distill up from "bad things could happen", it just isn't useful to keep paying people to come up with that kind of critique. And it isn't like we stopped paying attention to these concerns, is it? Nor were they completely blind siding us at the time. The question was largely of what to do about them.
The question also whether large-scale utilization of LLMs (and also the prerequisite increased training processes) should proceed before these issues were addressed. Clearly, we collectively answered "yes" without any actual reasoning (and arguably, without any collective decision making either).
For some of the concerns, like language understanding, I can't bring myself to think that many of the experts out there were doing any better than these models can do today. Quite the contrary.
And do you think that that would not have been counter to the concern over diversity of teams working on it?
Or concerns over bias going away by having the US attempt to abstain? Good luck with that. It sucks, but China and Russia should stand as stark examples that it turns out you can take strong control over the internet.
Earlier quoted context omitted.
Have you read through the sources on that Github link? It's a set of sociology cites establishing that bias exists (something no serious person ever disputed), followed by a couple papers showing mechanistic descriptions of how bias could propagate through an LLM. The paper you call out specifically takes last-generation open-weights models and attempts to trick them into revealing biases through their level of confi…
I confess I laughed harder at the Grok comment than I wish I had. Sad to remember that some strawmen are given life and promoted by people. Actively.
(Context: I asked it to write fake Reddit comments, because I was curious about how realistic they could be. The colorful phrase occurred during its reasoning about the requested subjects.)
Earlier quoted context omitted.
There has been plenty of research that shows LLMs encode social biases. It seems pretty obvious even before looking at the research that training on the whole internet will end up encoding widely-held social biases and stereotypes. https://arxiv.org/pdf/2508.07111 https://github.com/angl1n/social-bias-llm-vlm
> There has been plenty of research that shows LLMs encode social biases. At the risk of stepping into a hornets nest: is that different than "knowledge"? Or maybe, what would it mean if an LLM had no social biases? (Would we ever agree that was the case?)
Earlier quoted context omitted.
To conflate AI with smoking is just not helpful. At all. Or are you saying that there are acute harms from AI that are being ignored?
Acute, chronic - why would it matter? Why is it unhelpful to conflate AI with smoking? And yes, lots of people are saying "there are harms from AI that are being ignored".
What harms from AI are people ignoring?
The warnings: > 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. > The second warning was about bias amplification. The paper documented in detail that internet-scale training data contains systematic overrepresentation of dominant viewpoints…
There has been plenty of research that shows LLMs encode social biases. It seems pretty obvious even before looking at the research that training on the whole internet will end up encoding widely-held social biases and stereotypes. https://arxiv.org/pdf/2508.07111 https://github.com/angl1n/social-bias-llm-vlm
Bias could mean so, so many other things. Was the amyloid hypothesis incorrect? How should we use semicolons? How do you know when meetings waste more time than not? etc. People understand the world via mental shortcuts, via theory-rather-than-fact. We're stuck doing this because we're limited in so many ways. We are so biased about so many things, and this could interact in so many interesting ways. But damned if anyone cares about that. The only thing they seem to care about is how you feel about the "right" or "wrong" groups of people. It's a catastrophic waste of time and energy.
Some sensitive traits (e.g. Race) have high correlation with something we want to estimate (eg crime rate, credit score). The same traits can be correlated with thousands of different other attributes.
For example, to estimate the risk of loan default, (mathematically) i can use
a) race
b) zip code
c) 3 or 4 seemingly unrelated attributes, but still highly correlated to race
d) a few hundred attributes
e) a few million attributes, taking a PCA and trim down to a few hundred dimensions vector space
When does the discrimination begins or end? (a) is surely illegal, but you can argue (e) is still a proxy to the same thing.
There is no way to cut it fairly. It seems to me any kind of profiling should be illegal
It seems that the main issue with AI is often not what sci-fi or EA-adjacent prophets are trying to warn us about, but the insidious dangers of the failure modes. We are collectively not well calibrated to deal with systems that seems capable but fails in surprising ways. Commercial planes are still under the responsibility and control of highly trained human pilots, even if I am pretty sure that full automation woul…
I am pretty bullish on AI from a high level now, but one thing that recently hit me is how arbitrary and hacky the workflows with the various agents are. Sure, LLMs are not deterministic but now with agents and reasoning it seems like randomness squared.
The deafening silence in the comment section says it all.