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 <https://www.technologyreview.com/topic/artificial-intelligence/>
ARTIFICIALThis is scary INTELLIGENCE


paid to train AI are outsourcing their work. to AI


It's a practice that could introduce further errors into already error-prone
models.

By 

*	 <https://www.technologyreview.com/author/rhiannon-williams/>
Rhiannon Williamsarchive page

June 22, 2023



STEPHANIE ARNETT/MITTR | GETTY

A significant proportion of people paid to train AI models may be themselves
outsourcing that work to AI, a new study has found. 

It takes an incredible amount of data to train AI systems to perform
specific tasks accurately and reliably. Many companies pay gig workers on
platforms like
<https://www.technologyreview.com/2020/12/11/1014081/ai-machine-learning-cro
wd-gig-worker-problem-amazon-mechanical-turk/> Mechanical Turk to complete
tasks that are typically hard to automate, such as solving CAPTCHAs,
labeling data and annotating text. This data is then fed into AI models to
train them. The workers are poorly paid and are often expected to complete
lots of tasks very quickly. 


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<https://www.technologyreview.com/2023/04/04/1070938/we-are-hurtling-toward-
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<https://www.technologyreview.com/2023/04/04/1070938/we-are-hurtling-toward-
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Large language models are full of security vulnerabilities, yet they're
being embedded into tech products on a vast scale.

No wonder some of them may be turning to tools like ChatGPT to maximize
their earning potential. But how many? To find out, a team of researchers
from the Swiss Federal Institute of Technology (EPFL) hired 44 people on the
gig work platform Amazon Mechanical Turk to summarize 16 extracts from
medical research papers. Then they analyzed their responses using an AI
model they'd trained themselves that looks for telltale signals of ChatGPT
output, such as lack of variety in choice of words. They also extracted the
workers' keystrokes in a bid to work out whether they'd copied and pasted
their answers, an indicator that they'd generated their responses elsewhere.

They estimated that somewhere between 33% and 46% of the workers had used AI
models like OpenAI's ChatGPT. It's a percentage that's likely to grow even
higher as ChatGPT and other AI systems become more powerful and easily
accessible, according to the authors of the study, which has been shared on
<https://arxiv.org/pdf/2306.07899.pdf> arXiv and is yet to be peer-reviewed.


"I don't think it's the end of crowdsourcing platforms. It just changes the
dynamics," says Robert West, an assistant professor at EPFL, who coauthored
the study. 

Using AI-generated data to train AI could introduce further errors into
already error-prone models. Large language models regularly present false
information as fact. If they generate incorrect output that is itself used
to train other AI models, the errors can be absorbed by those models and
amplified over time, making it more and more difficult to work out their
origins, says Ilia Shumailov, a junior research fellow in computer science
at Oxford University, who was not involved in the project.

Even worse, there's no simple fix. "The problem is, when you're using
artificial data, you acquire the errors from the misunderstandings of the
models and statistical errors," he says. "You need to make sure that your
errors are not biasing the output of other models, and there's no simple way
to do that."

The study highlights the need for new ways to check whether data has been
produced by humans or AI. It also highlights one of the problems with tech
companies' tendency to rely on gig workers to do the vital work of tidying
up the data fed to AI systems.  

"I don't think everything will collapse," says West. "But I think the AI
community will have to investigate closely which tasks are most prone to
being automated and to work on ways to prevent this."

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by  <https://www.technologyreview.com/author/rhiannon-williams/> Rhiannon
Williams


 

 

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