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

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In summary, the conversation discusses an article from The Economist about poisoned datasets and the future of web-scale training sets. It references an arxiv article and considers whether data poisoning is a long-term issue, a start-up problem, or an overreaction. It is noted that data poisoning has been used in the past to trick search engines, and it is suggested that AI companies will need to program their AI to avoid certain patterns. The opinion is a mix of viewing data poisoning as both a long-term issue and an overreaction.
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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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1. What is a web-scale training set?

A web-scale training set is a large dataset that is used to train machine learning models. It typically consists of millions or even billions of data points collected from various sources on the internet.

2. Why is there concern about data poisoning in web-scale training sets?

Data poisoning refers to the intentional manipulation of data in a training set in order to compromise the performance of a machine learning model. With web-scale training sets, the sheer volume of data increases the risk of malicious actors injecting poisoned data into the set, which can lead to biased or inaccurate models.

3. What are the potential consequences of data poisoning in web-scale training sets?

Data poisoning can have serious consequences, such as compromising the accuracy and fairness of machine learning models. This can lead to biased decision-making and harmful outcomes, especially in sensitive areas like healthcare or finance.

4. How can we prevent data poisoning in web-scale training sets?

There are several strategies that can be used to prevent data poisoning in web-scale training sets. These include carefully vetting data sources, implementing security measures to detect and filter out poisoned data, and regularly monitoring and retraining models to detect and correct any biases that may have been introduced.

5. What role does transparency play in addressing data poisoning concerns in web-scale training sets?

Transparency is crucial in addressing data poisoning concerns in web-scale training sets. It allows for better understanding and scrutiny of the data and models being used, which can help identify and mitigate potential biases. Additionally, transparency can help build trust and accountability in the use of machine learning models.

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