Future of Web-Scale Training Sets: Unpacking Data Poisoning Concerns

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The discussion centers on the concept of poisoned datasets, highlighted in a recent article from The Economist. The conversation raises questions about the future of web-scale training sets and whether data poisoning is a temporary challenge, a long-term concern, or an overreaction. Historical context is provided, noting that data poisoning has roots in practices like keyword stuffing aimed at manipulating search engines. The consensus suggests that if AI companies rely on public web data, they will need to implement strategies to avoid problematic patterns, similar to existing search engine protocols. The overall viewpoint leans towards viewing data poisoning as both a significant issue and an overreaction, indicating a complex landscape for AI data management.
Frabjous
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I read an article in the April 6 edition of The Economist (regretfully behind a paywall) about poisoned datasets. Here’s an arxiv article it referenced.
https://arxiv.org/abs/2302.10149

What is the future of web-scale training sets? Is data poisoning a start-up pang, a long-term issue or an overreaction.
 
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Data poisoning started way back when with keyword stuffing to trick search engines. Nothing new here. If the public web is the source, AI companies will have to program their AI to avoid certain patterns like search engines already do today.

So I guess my opinion is a mix of long-term issue and overreaction.
 

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