What can blockchains do to ensure fairness?

Projects rooted in artificial intelligence (AI) are becoming an integral part of the modern technological paradigm, helping with decision-making processes across various sectors, from finance to healthcare. However, despite the significant progress, AI systems are not without their flaws. One of the most critical issues facing AI today is data bias, which refers to the presence of systemic errors in a given information set that lead to skewed results when training machine learning models.

Since AI systems rely on data; the quality of the input is of the utmost importance since any type of biased information can bias the system. This can perpetuate discrimination and inequality in society even further. It is therefore important to ensure the integrity and objectivity of data.

For example, a recent paper explores how AI-generated images, particularly those created from datasets dominated by American-influenced sources, can misrepresent and homogenize the cultural context of facial expressions. It mentions several examples of soldiers or warriors from different historical periods, all with the same American style.

Moreover, the pervasive bias not only fails to capture the diversity and nuances of human expression, but also risks erasing important cultural histories and meanings, thereby potentially affecting global mental health, well-being, and the richness of human experience. To reduce such bias, it is important to incorporate diverse and representative datasets into AI training processes.

Several factors contribute to biased data in AI systems. First, the collection process itself may be flawed, with samples that are not representative of the target population. This can lead to under-representation or over-representation of certain groups. Second, historical biases can creep into training data, which can perpetuate existing societal biases. For example, AI systems trained on biased historical data may continue to reinforce stereotypes of gender or race.

Finally, human biases can be inadvertently introduced during the data labeling process, as branding can contain unconscious biases. The selection of features or variables used in AI models can result in biased outcomes, as some features may be more correlated with certain groups, causing unfair treatment. To mitigate these problems, researchers and practitioners must be aware of potential sources of biased objectivity and actively work to eliminate them.

Can blockchain make objective artificial intelligence possible?

While blockchain technology can help with certain aspects of keeping AI systems neutral, it is by no means a panacea for eliminating bias entirely. AI systems, such as machine learning models, can develop certain discriminatory tendencies based on the data they are trained on. Additionally, if the training data contains different pre-dispositions, the system is likely to learn and reproduce them in its output.

That said, blockchain technology can help address AI biases in its own unique ways. For example, it can help ensure data provenance and transparency. Decentralized systems can trace the origin of the data used to train AI systems, ensuring transparency in the information collection and aggregation process. This can help stakeholders identify potential sources of bias and address them.

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Correspondingly, blockchains can facilitate secure and efficient data sharing between several parties, which enables the development of more diverse and representative data sets.

By decentralizing the training process, blockchain can also enable multiple parties to contribute their own information and expertise, which can help reduce the influence of a single partisan perspective.

Maintaining objective neutrality requires careful attention to the various stages of AI development, including data collection, model training and evaluation. In addition, continuous monitoring and updating of AI systems is essential to deal with potential biases that may arise over time.

To gain a deeper understanding of whether blockchain technology can make AI systems completely neutral, Cointelegraph contacted Ben Goertzel, founder and CEO of SingularityNET – a project that combines artificial intelligence and blockchain.

In his view, the concept of “complete objectivity” is not really useful in the context of finite intelligence systems that analyze finite data sets.

“What blockchain and Web3 systems can offer is not complete objectivity or lack of bias, but rather transparency so that users can clearly see the bias of an AI system. It also offers open configurability so that a user community can customize an AI model to have the kind of bias it prefers and transparently see what kind of bias it reflects, he said.

He further stated that in AI research, “bias” is not a dirty word. Instead, it is simply an indication of the orientation of an AI system looking for certain patterns in data. That said, Goertzel admitted that opaque biases imposed by centralized organizations on users who aren’t aware of them — but are nevertheless guided and influenced by them — are something people need to be wary of. He said:

“Most popular AI algorithms, such as ChatGPT, are poor at transparency and revealing their own biases. So part of what is needed to properly address the AI ​​bias problem is decentralized participatory networks and open models , not just open source, but open weight matrices that are trained, custom models with open content.”

Similarly, Dan Peterson, COO of Tenet – an AI-focused blockchain network – told Cointelegraph that it is difficult to quantify neutrality and that some AI values ​​cannot be objective because there is no quantifiable line for when a dataset loses neutrality. In his view, it ultimately boils down to the perspective of where the engineer draws the line, and that line can vary from person to person.

“The concept of everything being truly ‘untouched’ has historically been a difficult challenge to overcome. While absolute truth in any data set fed into generative AI systems can be difficult to determine, what we can do is leverage the tools made more readily available to us through the use of blockchain and Web3 technology,” he said.

Peterson stated that techniques built around distributed systems, verifiability and even social proofing can help us develop AI systems that come “as close” to absolute truth. “However, it is not yet a turnkey solution; these evolving technologies are helping us move the needle forward at breakneck speed as we continue to build tomorrow’s systems,” he said.

Looking towards an AI-powered future

Scalability remains a significant concern for blockchain technology. As the number of users and transactions increases, it may limit the ability of blockchain solutions to handle the huge amounts of data generated and processed by AI systems. Moreover, even using and integrating blockchain-based solutions into existing AIs poses significant challenges.

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First, there is a lack of understanding and expertise in both AI and blockchain technologies, which can hinder the development and deployment of solutions that combine both paradigms effectively. Second, convincing stakeholders of the benefits of blockchain platforms, especially when it comes to ensuring unbiased AI data transfer, can be challenging, at least initially.

Despite these challenges, blockchain technology has enormous potential in smoothing the rapidly evolving AI landscape. By leveraging key features of the blockchain – such as decentralization, transparency and immutability – it is possible to reduce biases in data collection, management and labeling, ultimately leading to fairer AI systems. Therefore, it will be interesting to see how the future continues to develop from here.