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What do you get when you combine artificial intelligence and web3? In theory, you get the best of both worlds: the incredible cognitive capability of AI coupled with the openness and universal access of blockchain. It’s a combination that’s ideal for collaboration and innovation to flourish, and web3 imagineers have wasted no time in porting the best bits of AI onto blockchain rails.
But AI is more than just a benign technology: it’s a full-blown revolution that, like other human-driven movements through history, comes with its behavioral issues and concerns. From disrespecting data privacy to algorithmic bias, AI is like a precocious kid who acts out in front of the class. Genius? Undoubtedly. Erratic? Absolutely.
When artificial intelligence is powered by web3 technology, many of these problems are magnified – while others are mitigated. However, blockchain is an industry whose big thinkers are invariably confident that they have a solution to everything. Can they tame the AI beast or are they destined to inherit the same issues that have dogged artificial intelligence throughout its short history?
The Ethics Issues That Won’t Leave AI Alone
It seems strange to be discussing very human qualities in the context of unfeeling machines, but the more they imitate us, the more they exhibit those same imperfections that have been our Achilles heel since the dawn of time. First god made man in his image; then man made machines in his. And now we’re living through an era of hybrid symbiotism where the lines between man and machine are increasingly blurred and it’s fast becoming impossible to tell who’s a bot and who’s not.
While the broad consensus is that AI is a net good for society, there are certain areas where it comes into conflict with the laws, both written and social, governing the way we live, work, and recreate. Specifically, AI stands accused of plagiarism, data sharing, algorithmic bias, and disregarding privacy. Let’s consider the accusations and examine web3’s potential to ameliorate or exacerbate these issues.
AI Copied My Homework
The plagiarism accusations leveled against AI are the most flagrant crimes it stands accused of – though of course, it is the human creators who have transgressed, even if it’s artificial intelligence carrying the can. In one of the most egregious examples, Clearview AI was hit with multiple lawsuits for scraping billions of images from social media without consent and passing them on to law enforcement to use in facial recognition software, which was sold to law enforcement agencies.
Most of the time, however, the plagiarism is more subtle; AI developers use copyrighted content to train models without permission; and generative AI contravenes music copyright, resulting in “new” songs that sound uncannily like classic tracks. Sometimes when AI is busted, it’s plain to see, such as when Adobe watermarks show up in generative art. But most of the time, the copying is harder to prove yet undeniably endemic.
Subtler still is the algorithmic bias that can creep into systems trained on large datasets. The accusation most commonly made against AI in this context concerns machine learning algorithms used to determine attributes such as creditworthiness, which risk excluding certain demographics and perpetuating economic inequalities. But as with much of the plagiarism accusations AI faces, suspicion is one thing; proof is quite another.
Was an individual excluded from a particular system on account of their race or was it their poor credit rating? AI knows the truth but doesn’t expect to get a straight answer from it – being economical with the truth is another human trait it’s inherited.
The final charge that hangs heavy over AI, intermingling with that of plagiarism, concerns the lack of data privacy. AI is reliant on large datasets for machine learning: this is the brain fuel that makes artificial intelligence so darn intelligent. However such datasets can include financial transaction histories, personal identification information, and other sensitive data, raising major ethical concerns about individuals’ right to privacy.
So what’s the solution to all this and does the intervention of web3 have the potential to mitigate these issues or further muddy the waters?
Injecting Ethics With Decentralized Technology
The first thing that responsible companies operating at the intersection of AI and web3 must do is acknowledge the magnitude of the challenge. Supporting AI innovation by expanding datasets, streamlining information sharing, and creating tokenized marketplaces for training data needs to be balanced with an obligation to handle user data ethically, respect intellectual property, and maintain personal privacy.
And with all due credit to the industry, there are clear signs that it’s attempting to do just that. 0G’s decentralized AI operating system, for example, is underpinned by a firm commitment to maintaining ethical AI development. It’s one of several web3 AI companies convinced that the blockchain industry has the technology to solve the drawbacks of AI; using ZK proofs, for example, to allow encrypted data to be processed. Acting ethically in this context isn’t just “the right thing to do”: it’s the best way to distinguish web3 from web2; the future from the past.
Other web3/AI projects are specifically focused on attribution, to encourage a shift away from “dirty models” trained on scraped data to clean datasets that ensure IP owners are fairly remunerated. This can be achieved through tokenization and microtransactions, allowing content creators to receive royalties every time their data is used. With blockchain keeping a verifiable record of data usage and smart contracts automating payments, it’s possible to create an entirely self-sustaining system that is theoretically free of bias.
When Future Tech Collides
The convergence of AI and blockchain offers both promise and peril. Decentralized solutions can mitigate key ethical issues by improving transparency and reducing centralized control, but they also risk introducing new ethical challenges such as lack of accountability and security risks.
Because web3 is a sector that is hard to regulate due to its decentralized and borderless nature, the onus is on the industry to self-regulate. This means demonstrating that it not only has the technology to improve upon centralized AI, but to raise the bar for ethical behavior in the process. Doing so won’t just improve AI’s reputation – it will allow web3 to claim the moral high ground while demonstrating that it’s possible to innovate while protecting users and promoting fairness.
Disclaimer: This article is provided for informational purposes only. It is not offered or intended to be used as legal, tax, investment, financial, or other advice.
