Diversity is the key against AI bias

As AI continues to revolutionize our daily lives and workplaces, the issue of data bias has emerged as a significant challenge. As we venture towards a future dominated by Web3, we expect to see a surge of innovative products and services that leverage AI and Web3 in tandem. Despite some experts' opinions that decentralized technologies can address data bias, the reality is far from that.

Web3's market size remains relatively small and challenging to determine due to its nascent development stage and its definition's fluidity. Although the market size was estimated to be around $2 billion in 2021, various analysts and research firms have projected a compound annual growth rate (CAGR) of approximately 45%. Combined with the rapid growth in Web3 solutions and consumer adoption, the market value is projected to hit $80 billion by 2030.

Despite the industry's rapid expansion, current affairs and other tech industry factors have led to AI data bias going down the wrong path.

What contributes to bias?

AI systems heavily rely on a vast amount of superior-quality data to train their algorithms effectively. One such model, the ChatGPT, is a part of OpenAI's GPT-3, which was trained on a mammoth corpus of high-quality data. Although OpenAI has not disclosed the exact size of the training dataset, it is believed to be hundreds of billions of words or more.

This vast quantity of data was thoroughly filtered and preprocessed to ensure its relevance and high quality for the language generation task. OpenAI utilized advanced machine learning techniques such as transformers to train the model on this massive dataset, enabling it to learn complex patterns and relationships between words and phrases and generate high-quality text.

The data quality used for training an ML model significantly impacts its performance. The dataset's size is also crucial in determining the model's generalization ability to new data and tasks. However, it is essential to note that both the quality and quantity of data can contribute significantly to data bias.

The lack of diversity

Another reason why Web3 increases AI data bias is due to the lack of diversity in the population that uses Web3 solutions. As Web3 solutions are still in their early stages of development, they tend to be used by a limited number of individuals who are familiar with the technology and have the resources to access it.

This population may not represent the wider population, leading to biased data being used to train AI algorithms. For example, if a decentralized platform for financial transactions is only used by a specific demographic, such as young, affluent individuals, the data used to train the AI algorithm will only reflect this population's financial behavior. They may not apply to other groups.

Furthermore, the lack of regulation and standards in the Web3 space can also contribute to data bias. As there are no standardized protocols for collecting and processing data, there is a risk that data may be collected and analyzed in a biased manner, leading to biased AI algorithms.

Prioritizing diversity and inclusion in developing and using Web3 solutions is essential. This includes ensuring that data is collected from diverse sources and that AI algorithms are regularly audited for bias. It also means developing standards and regulations for data collection and processing to minimize bias.

The lack of diversity in the Web3 industry is a significant concern. Women and people of color are significantly underrepresented, with less than 5% of Web3 startups having a female founder. This imbalance increases the likelihood of AI data bias being ignored as an issue by male and Caucasian founders.

The Web3 industry must prioritize diversity and inclusiveness in its data sources and teams. Diverse perspectives are more likely to create products and services that work for a global audience, leading to higher returns and scalability. Moreover, the industry must focus on data quality and accuracy, ensuring unbiased data for training AI algorithms.

Changing the narrative around diversity, equality, and inclusion is also critical. From a financial and scalability perspective, it is essential to understand that diversity is not just a moral or ethical issue but a strategic one. Startups with diverse teams are more likely to have a broad perspective and a better understanding of the needs and preferences of their customers. As a result, they are more likely to create successful products and services on a global scale.

Finding broad data sources

In order to address the issue of data bias in AI systems, decentralized data marketplaces and blockchain technology can be used to ensure the transparent and accurate exchange of data. However, it may take years to find broad data sources until a mainstream audience uses Web3 solutions.

Currently, Web3 solutions appeal mainly to people in the startup and tech communities, which lack diversity and represent only a small percentage of the global population. Until these solutions become more mainstream and accessible to a broader population, high-quality and diverse data may be difficult to obtain, which could hinder the training of AI systems.

Therefore, the Web3 industry must prioritize diversity and inclusiveness in its data sources and teams. This will help mitigate the risk of biased data and result in products and services that work for billions of customers, leading to high returns and global scalability. The industry must take steps now to ensure that the data used to train AI algorithms is free from bias, accurate, and of high quality.