Based on material from the website - By crypto.news
At first glance, AI and blockchain have a lot in common. Both technologies are transformative, capable of reshaping any industry they touch. Both have attracted huge investments, not to mention the hype surrounding them. And both are crude tools whose full power is revealed only through skilled use and precise application.
When AI and blockchain are wisely combined, they can work wonders. AI virtually reduces the marginal costs of intelligence to zero, while blockchain virtually reduces the marginal costs of coordination to zero, ensuring abundance. Intelligent autonomous systems. Verifiable frameworks for tracking data and content attribution. A circular economy for distributing digital resources. But that’s not all this pair has to offer. With a sensible combination of blockchain and artificial intelligence, it can eliminate the most serious flaws of the latter. After all, without a doubt, AI in its current form is full of such flaws.
Artificial intelligence is rapidly changing industries, from automating routine tasks to enhancing customer service. However, as AI becomes more deeply embedded in the process of making critical decisions, from healthcare to transportation, alarm bells are ringing louder regarding transparency and accountability. Bias, manipulation, and opaque decisions threaten to undermine trust in AI, eroding its enormous potential. This is where blockchain has a chance to shine. With a decentralized, immutable ledger serving as a foundation for truth, AI can gain the verifiability and ethics it currently lacks. Blockchain restores trust in a technology that is currently devoid of it.
AI bias is like climate change: everywhere and yet nowhere at the same time. It’s impossible to refute, but often hard to pinpoint what exactly it consists of. Sometimes its glaring feature, like the Google Gemini tool that creates completely inaccurate historical images. However, more often than not, we only have a sense that something is amiss, and there’s no easy way to prove it, let alone fix it (for example, just a few weeks ago, Deepseek R1 claimed that Trump was the previous president of America). And let’s not even talk about ‘fabricated consistency’ when AI supposedly tries to please while secretly pursuing its own goals.
In addition to bias, backdoor attacks pose a more serious threat. Malicious actors can inject hidden triggers during the training of AI, causing it to behave incorrectly, for example, misclassifying images according to certain patterns upon activation. Such vulnerabilities risk jeopardizing the operation of systems in real-time, and fixing this is not easy. It is telling that as AI becomes more human-like, it inherits our worst habits, including the ability to deceive, and then, when necessary, to lie even more.
It’s one thing when AI makes mistakes in generating images, and quite another when an autonomous driving algorithm ignores a stop sign. And that's not even the worst that can happen when AI fails.
In critical safety areas such as aviation and robotics, the reliability of AI is beyond question. Aviation increasingly relies on AI for air traffic management, predictive maintenance, and autopilot systems. An error caused by a biased or hacked algorithm can be fatal. Although AI excels at predicting mechanical failures, saving billions of dollars in downtime, its reliability requires oversight. AI diagnostic tools in aviation can fail, misinterpreting data if they are trained on incorrect datasets. Public safety depends on transparent and accountable AI — without it, trust and lives are at stake.
When cars first appeared, accidents were not uncommon, but they rarely resulted in fatalities due to low speeds and a small number of vehicles on the roads. But as the automotive industry gained momentum and engines became more powerful, safety measures were needed to reduce the number of traffic accidents. Currently, AI is at the Model T stage: a turning point whose final form has yet to be realized. As artificial intelligence gains traction and becomes widely integrated, the risk of failure or bias increases exponentially. That’s why now is the time to act to fix AI’s shortcomings, and this is where blockchain can prove invaluable.
There is another area where blockchain excels with AI, and which we still need to address: governance. AI without proper governance risks going out of control, making unchecked decisions that will slip under the radar. Blockchain counters this by offering a decentralized governance structure that is accountable and (there's that word again) verifiable.
Smart contracts can encode ethical standards, ensuring fairness and transparency in the development of AI. They can require the provision of unbiased data for training or flag discrepancies, halting the deployment of a model until corrected. Blockchain also provides stakeholders, such as developers and users, the opportunity to participate in governance, voting on the formulation of AI rules. This collective oversight curbs overreach into autonomous systems, promoting accountability where traditional systems fall short.
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