Speaking to crypto.news, Matthijs de Vries, CEO and founder of Nuklai, delved into the ethical implications of AI use in the NFT sector.
The rise of AI has opened up many new possibilities, and one of the sectors that has leveraged this technology heavily is NFTs. From generating NFT art to improving verification processes, AI is becoming a crucial tool in the decentralized digital art world.
However, this rapid integration also poses some ethical problems. Issues such as intellectual property rights, the potential for misuse of AI-generated content, and the transparency of AI algorithms are at the forefront of this debate.
The need for ethical guidelines and robust policies becomes more important as AI’s influence in the NFT space grows. Balancing innovation and ethical considerations will be critical to fostering a sustainable and reliable ecosystem.
De Vries sees AI as a transformative force for improving NFT authentication and security, but he argues that addressing its ethical challenges is crucial to maintaining a trustworthy and sustainable digital art ecosystem.
AI has been criticized for copyright issues and is also seen as a solution to NFT copyright issues. Given its own copyright challenges, how can AI effectively address these issues?
Technology can be a double-edged sword. For example, AI’s generative models have both helped and hurt. They have been used to copy artists’ work without permission. This abuse is quite common in scams. A scammer’s unauthorized use of an artist’s work often results in AI-generated creations that are similar to or indistinguishable from the artist’s original piece. Such creations fuel the conversation about property rights violations and illustrate the need for stricter regulations in AI development. At the same time, AI algorithms can detect derivative works and forgeries, even if they exhibit subtle changes. People can miss this one. For example, AI can learn an artist’s style and then identify copies. This capability is critical when addressing copyright issues.
There are also some ethical concerns. These include intellectual property and potential misuse of AI-created content. How should platforms address these concerns to maintain trust and integrity?
AI needs specific data for training. Artists can embed ownership information into the NFT that represents a work of art. This clear traceability back to the original maker ensures that anyone can verify the piece’s ownership. Platforms can use AI to scan that data and scour the internet for pieces that attempt to replicate the creator’s art. If it finds similar pieces, it can check the authentication information. It will flag any discrepancies and help artists enforce their IP rights. Platforms can also distribute automatic royalty payments based on the permitted use of the art.
This system ensures fair payment and tracks data use in blockchains. It protects the rights of creators and encourages ethical use of content. Additionally, an NFT marketplace with advanced AI protects artists from property misuse and buyers from fake art. These steps reduce scams and increase the trust and integrity of the platform.
From your experience, what progress is being made towards real-time NFT verification using AI?
Given information about the provenance of NFTs, AI can process these massive amounts of data to verify a real or fake NFT in near real time. We can train AI to recognize unique features found only in authentic NFTs. This quick verification prevents fraudulent listings and can warn users before purchasing potentially counterfeit products. It helps prevent the sale of counterfeit or even stolen NFTs.
How do you think these improvements will impact the user experience?
As AI gets better at detecting this information, it can expand its capabilities beyond just identifying fake NFTs. For example, AI can be used to find volume peaks for a particular NFT listing. It can also flag multiple NFT listings with similar characteristics. This would shut them down before anyone has a chance to buy. NFT marketplaces all run on blockchain networks, which are known for their open-source nature. A reputable NFT marketplace will make the AI’s knowledge public for anyone to view, allowing buyers to view the history of an NFT. This is not to say that blockchains are immutable, meaning users can rest assured that an NFT’s data has not been tampered with.
More and more people are using AI-driven systems to verify the origin of NFTs. How do these systems guarantee the authenticity of digital assets?
To verify that a digital asset is authentic, AI requires a robust data trail to determine provenance and ownership. Public data sources provide a verifiable trail of authenticity. They are the ideal tool for training AI because they show the many ways fraudsters try to game the system. Data collaboration and on-chain verification can add significant value to AI digital asset valuation. AI can also evaluate real-world assets (RWAs) and intellectual property rights.
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What do you see as some of the key challenges in keeping data accurate and preventing fraud?
Of course, bad actors will continue to find ways to circumvent existing systems. This is why public collaboration is critical, as expanding trainable data will help AI detect new fraud methods as they emerge. AI training also needs accurate data. This requires NFT owners to properly document the history of their assets. As long as the human side of NFTs is correct, so will the AI findings. When it comes to privacy, AI can learn what information should be shared and what should be kept private. This boils down to NFT developers and marketplaces building their systems in a way that promotes artist privacy. AI does not decide what information should be private or not – that is up to humans.
Digital product passports (DPPs) are a growing concept. It aims to track the history and ownership of luxury items and NFTs. How do AI and blockchain increase the security and authenticity of DPPs?
Digital passports can be easily created by checking and tracking all supply chain data. This data is then placed in an NFT to prove its origin. It keeps track of everything such as carbon footprint, ownership and maintenance. AI models can then detect fraud by finding unusual patterns. AI can crawl the web faster than humans and doesn’t need rest. Essentially, AI can monitor multiple NFTs 24/7 and immediately flag NFTs with inauthentic DPPs. However, AI works best with publicly available data. Blockchain-powered supply chains are completely transparent. They enable AI to understand their inner workings and spot discrepancies, making them more effective at tracking NFTs.
Finally, can you explain to our readers how neural networks and machine learning make NFT authentication more accurate and efficient?
Anyone could replicate an NFT collection and create a counterfeit version, but the underlying data would show that it is not the original. Neural networks analyze everything from the NFT’s metadata to the style of its creator in ways the human eye could never detect. Hackers and scammers are getting smarter. They are constantly coming up with new ways to deceive people. But AI can reliably validate the authenticity of an NFT when trained on diverse data sets and combat new methods that a bad actor comes up with. Distinguishing counterfeits is difficult for most people – and training AI on large data sets makes detecting fraud easier. Technological advancements such as neural networks are increasing the ability to integrate comprehensive verification methodologies into NFT marketplaces.
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