> It pays okay ($45+/hour)

For reference, the median hourly wage is $27/hour.

https://nationalequityatlas.org/indicators/Wages_Median

I wouldn't mind this work at that pay, being particularly strong in leetcode and in CS itself.

How do I join?

How often do encounter difficult content? Like gore, violence, hate, etc.? I would think prompts would keep that out of responses, is that naive of me?

hmm, this feels like ScaleAI

Is something stronger than your wish to get a regular job tying you to where you currently live?

Yeah, you can see this with Google's search results too. They're trying to improve on some internal metric, but the metric was clearly generated from ratings by people ignorant of the topics. And so the search results get worse, but appear better internally.

Great to see that they have not learned from this experience, and are repeating the mistake with Gemini.

How is this not Quest Diagnostics slipping into Theranos territory, buttressed by a hidden factory of typists?

This reminds me of the early voice-to-text start ups in the 00's that had these miraculous demos that required people in call centers to type it all up and pretend it was machine.

This is great!

Are there companies that focus on labeling of inputs rather than RLHF of outputs?

> Are all of the LLMs trained using human labor that is sometimes exposed to extreme content?

The business process outsourcing companies labelling things for AI training are often the same outsourcing companies providing moderation services to facebook and other social media companies.

I need 100k images labelled by the type of flower shown, for my flower-identifying AI, so I contract a business that does that sort of thing.

Facebook need 100k flagged images labelled by is-it-an-isis-beheading-video to keep on top of human reviews for their moderation queues. They contract with the same business.

The outsourcing company rotates workers between tasks, so nobody has to be on isis beheading videos for a whole shift.

One of the key innovations behind the DNN/CNN models was Mechanical Turk. OpenAI used a similar system extensively to improve the early GPT models. I would not be surprised that the practice continues today; NN models needs a lot of quality ground truth training data.

I have been a generalist annotator for some of the others you mentioned, due to NDA will not specify which. I would venture to guess that basically all major models use some degree of human feedback if there is money coming in from somewhere.

I've shared this once on HN before, but it's very relevant to this question and just a really great article so I'll reshare it here:

https://www.theverge.com/features/23764584/ai-artificial-int...

it explores the world of outsourced labeling work. Unfortunately hard numbers on the number of people involved are hard to come by because as the article notes:

"This tangled supply chain is deliberately hard to map. According to people in the industry, the companies buying the data demand strict confidentiality. (This is the reason Scale cited to explain why Remotasks has a different name.) Annotation reveals too much about the systems being developed, and the huge number of workers required makes leaks difficult to prevent. Annotators are warned repeatedly not to tell anyone about their jobs, not even their friends and co-workers, but corporate aliases, project code names, and, crucially, the extreme division of labor ensure they don’t have enough information about them to talk even if they wanted to. (Most workers requested pseudonyms for fear of being booted from the platforms.) Consequently, there are no granular estimates of the number of people who work in annotation, but it is a lot, and it is growing. A recent Google Research paper gave an order-of-magnitude figure of “millions” with the potential to become “billions.” "

I too would love to know more about how much human effort is going into labeling and feedback for each of these models, it would be interesting to know.

Stupid question... How can we build on these models without the humans doing all this work?

Even theoretically.

So why do you think asking this question here would yield a satisfying answer, especially how the HN community likes to dispute any vague conclusions for anything as hyped as AI training?

To counter your question, what makes you think that's not the case? Do you think Mistral/Moonshot/Qwen/etc. are all employing their own data labelers? Why would you expect this kind of transparency from for-profit bodies that are evaluated in the billions?

RLHF (and its evolution, RLAIF) is actually used for more than setting "values and preferences". It's what makes AI models engage in recognizable behavior, as opposed to simply continuing a given text. It's how the "Chat" part of "ChatGPT" can be made to work in the first place.

> Sawyer is one among the thousands of AI workers contracted for Google through Japanese conglomerate Hitachi’s GlobalLogic to rate and moderate the output of Google’s AI products...

Depends how you look at it. I think a brand like Google should vet a mere one level down the supply chain.

What is a "human value" and whose preferences?

Isn't that mostly the fine-tuning phase? RLHF being cherry on top?

> to ensure the AI models are more aligned with human values and preferences.

to ensure the AI models are more aligned with Google's values and preferences.

FTFY

> to ensure the AI models are more aligned with human values and preferences.

And which are these universal human values and preferences ? Or are we talking about silicon valley's executives values ?

Because they are doing it to compute quality metrics not to implement RLHF. It’s not training data.

> For this to be true they would have to throw away labeled training data.

That's how validation works.

Key word: "directly"

It does so indirectly, so it's a true albeit misleading statement.

They probably don’t do it at a scale large enough to do RLHF with it, but it’s still useful feedback the people working on the projects / products.

It's employment at will. They are free to go work somewhere else if they don't like it...

Can you believe that companies would ask people to do things they normally wouldn't in exchange for money!?

These types of articles always have an elitist view of the workers hired. That's a big source of the right (in the US) despising the left. The left don't say it directly, but when they talk about how shitty their town is and how the job they have is exploitative, there's an implicit judgment on the persons who live/work there.

I agree, article is pretty low quality ragebait. Not good journalism at all.

Not so easy. What if you get hired as a physiotherapist somewhere but on your first day you find out you will work in a brothel?

Or join an hospital as nurse, but then you are asked to perform surgery as you were a doctor?

There are serious issues outlined in the article.

Scale AI’s entire business model was using people in developing countries to label data for training models. Once you look into it, it comes across as rather predatory.

This was one of the first links I found re: Scale’s labor practices https://techcrunch.com/2025/01/22/scale-ai-is-facing-a-third...

Here’s another: https://relationaldemocracy.medium.com/an-authoritarian-work...

Couple of months ago I received a job invite for Kotlin AI trainers from the team at Upwork. I asked what the job is about and she says something like "for the opportunity to review & evaluate content for generative AI." And I'm from a developed country too.

Karen Hao's recent book "Empire of AI" about the rise of OpenAI goes into detail how people in Africa and South America were hired (and arguably exploited) for their training efforts.

There's a YouTube video titled "AI is a hype-fueled dumpster fire" [0] that mentions OpenAI's shenanigans. I haven't fact checked that but I've heard enough stories to believe it.

[0] https://youtu.be/0bF_AQvHs1M?si=rpMG2CY3TxnG3EYQ

There's nontrivial historical precedent for this exact playbook: when a new paradigm (Lisp machines and GOFAI search, GPU backprop, softmax self-attention) is scaling fast, a lot of promises get made, a lot of national security money gets involved, and AI Summer is just balmy.

But the next paradigm breakthrough is hard to forecast, and the current paradigm's asymptote is just as hard to predict, so it's +EV to say "tomorrow" and "forever".

When the second becomes clear before the first, you turk and expert label like it's 1988 and pray that the next paradigm breakthrough is soon, you bridge the gap with expert labeling and compute until it works or you run out of money and the DoD guy stops taking your calls. AI Winter is cold.

And just like Game of Thrones, no I mean no one, not Altman, not Amodei, not Allah Most Blessed knows when the seasons in A Song of Math and Grift will change.