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Web3 negative side: how decentralization increases bias in AI

Thanks to artificial intelligence, there have been quick changes in how we live and work (AI). Yet, people are paying more attention to the issue of bias in AI data. As we move toward a Web3 future, it makes sense that we will see new, cutting-edge products, solutions, and services that use both Web3 and AI. Also, contrary to what some commenters have said, decentralized technologies are not the answer to data bias. In this section, we will review the negative effects of web 3 and AI in all metaverse news.

Even though the Web3 ecosystem is still growing and the definition of Web3 is always changing, the size of the Web3 market is still very small and hard to figure out. The size of the market was estimated to be close to $2 billion in 2021, but analysts and research companies have reported an expected compound annual growth rate (CAGR) of about 45%. This, along with the fast development of Web3 solutions and consumer adoption, puts the Web3 market on track to be worth about $80 billion by 2030.

Although it is growing quickly, bias in AI data is going in the wrong direction because of how the market is right now and other factors in the tech sector.

The connection between quantity, quality, and bias

AI systems need a lot of good data to teach their algorithms what to do. The ChatGPT model, which is part of OpenAI’s GPT-3, was trained with a huge amount of high-quality data. OpenAI hasn’t said exactly how much data is used for training, but it’s likely that it’s in the hundreds of billions of words or more.

The data was filtered and preprocessed to make sure it was of the best quality and could be used for the task of making languages. With this huge dataset, OpenAI trained the model with cutting-edge machine learning (ML) techniques like transformers, which helped it find relationships and patterns between words and phrases and produce high-quality text.

The quality of the AI training data has a big effect on how well an ML model works. The size of the dataset can also have a big effect on how well the model works with new data and tasks. Still, it is also true that both the quality and quantity of data have a big effect on data bias.

unique bias risk

Bias in AI is a big worry because it can lead to unfair, discriminatory, and harmful results in fields like employment, credit, housing, and criminal justice, among others.

In 2018, Amazon had to get rid of an AI recruiting tool that showed bias against women. Since the AI was trained on resumes sent to Amazon over a ten-year period, most of which were for men, it gave less weight to applications that used the words “female” and “woman.”

And in 2019, scientists found that an AI model that was sold to the public and used to predict health outcomes was biased against people of color. Because it was made with data from mostly white patients, the algorithm is more likely to give false positives for Black patients.

Because Web3 solutions aren’t centralized and AI isn’t either, there is a unique risk of bias. Since there aren’t many Web3 solutions and fewer people can use them, it can be hard to train AI algorithms well in this setting because the data isn’t as good and there isn’t as much of it.

We can compare this to the way that companies like 23andMe collect genomic information, which favors people from poor and underrepresented groups. People with low incomes or who live in areas where the service doesn’t operate, which tends to be in poorer, less developed countries, have a harder time getting DNA testing services like 23andMe because of their cost, availability, and targeted marketing.

Because of this, genetic research and the improvement of health care and medicine may be skewed because the data collected by these companies may not reflect the genomic diversity of the larger population as well as it could.

This brings up yet another way in which Web3 makes AI data more biased.

Discrimination in the business world and a focus on ethics

One big worry is that there isn’t enough variety in the Web3 startup sector. By the year 2022, 26.7% of tech jobs will be held by women. 56% of these are women of color. Women are much less likely to be in leadership positions in technology than men.

Web3 makes this difference between them bigger. Various observers say that less than 5% of Web3 startups are started by women. Because there isn’t much diversity, it’s likely that founders who are men and from Europe won’t notice that AI data is biased.

To get around these problems, the Web3 industry must put a high value on diversity and inclusion in both its data sources and its staff. The industry also needs to change how it talks about how important diversity, equality, and inclusion are.

From a financial and scalability point of view, a startup with a diverse staff is more likely to have high returns and the ability to work on a global scale. This is because products and services made from different points of view are more likely to work for billions of customers rather than millions. So that the data used to train AI algorithms is fair, the Web3 sector also needs to focus on the quality and accuracy of data.

Does Web3 offer a way to fix the problem of bias in AI data?

One way to solve these problems is to make decentralized data marketplaces that let people and businesses share information in a safe, open way. So, AI algorithms can be trained with a wider range of data, making it less likely that the data is biased. Blockchain technology can also be used to make sure that data is correct and transparent and to stop algorithms from being biased.

But if Web3 solutions aren’t widely used, it will be very hard to find large data sources for many years.

Even though Web3 and blockchain are still talked about a lot in the news, these products and services are most likely to appeal to people in the startup and tech communities, which aren’t very diverse and only make up a small percentage of the world’s population.

It is hard to figure out how many people in the world work for startups. In the last few years, the business has created almost three million jobs in the United States. If you look at the tech industry as a whole and don’t take into account jobs that have been lost, it doesn’t even come close to being a good representation of people who are of working age.

Access to enough high-quality data to train AI systems will continue to be a big problem until Web3 solutions become more popular, can be used by people who don’t have a natural interest in technology, and are cheaper and easier for a larger number of people to use. This problem needs to be fixed as soon as possible by the sector.

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