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Artificial intelligence has become a powerful force in the finance ecosystem, providing faster and data information that promises to improve investments, loans and risk management. AI advisers who personalize financial strategies for companies and individuals with very advanced trading systems that make decisions based on microsecond data, the AI financial sector has a lot of space to develop.
But there is a major problem: the bias.
Despite speed, precision and what seems to be an objectivity, the financial AI systems carry the same angle that the industry has been trying to eliminate for decades. For example, according to the University of Lehigh, the GPT -4 GPT -4 OPENAI language models – simulating a mortgage advisor or a decision -making decision system – said that some candidates’ demographics have 120 higher credit points as white candidates to receive the same approval despite the same income, credit history and debt levels.
This bias does not only affect traditional financial markets but also decentralized financial ecosystems and cryptography. Take for example the market forecast platforms supplied by the AI. Given that their data is based on price history, the feeling of news or social trends, these platforms could sometimes react excessively to market anomalies – Crypto is full of black swan events such as the collapse of Terra, the crash FTX or the major penalties of regulators.
Consequently, these prediction tools can become too aggressive or even overweight and social gossip, leading to bad signals and predictions.
Blockchain, xai at the rescue
The limits and opaque nature of many AI systems prevent them from becoming entirely transparent and responsible. Some even call them black boxes, because AI models have generally have little or no transparency.
In particular, the decisions taken by AI tools in cryptographic space are generally not explainable – this makes it difficult for users to understand how decisions are made. The absence of standardized audit protocols for AI systems would also lead to incoherent assessments and potential monitoring of critical problems.
The integration of blockchain technology with an explainable AI, or XAI to be short, can attack this problem by providing immutability and transparency that also accompany decentralized books-potentially improving audit methods, because listeners will have full access to the data of the platform and underlying algorithms.
XAI models already draw increased attention because they ensure that the decision -making process is fair and ethical in addition to being effective. Blockchain technology can complete XAI equity by creating immutable records of AI decision -making processes, ensuring that each action is traceable and verifiable. This will promote confidence and responsibility.
Blockchains operate in a without confidence. This does not mean that technology cannot trust, but it suggests that third parties or central authorities will not be necessary to confirm decisions. Decentralization removes the need for a centralized entity to supervise the processes, thanks to intelligent contracts that operate independently.
When a model modifies or produces a decision, the lack of newspapers and version control can cause confidence problems with most AI platforms. Times of blockchain technology recordings and data on a large unchanging book.
Fico, a credit rating company, used blockchain to record the decisions of the AI model, so that regulators can draw the way decisions such as credit approvals have been made. The company received the “Tech of the Future – Blockchain and Tokénisation” prize at Banking Tech Awards in London last year.
From theory to practice
Blockchains and decentralized financing protocols have the opportunity to cook equity, transparency and responsibility in AI models – something with traditional financial companies with.
XAI’s combination with chain verification can transform how decisions are made and trust the web 3 ecosystem. For example, the use of XAI to explain the vote of decentralized autonomous organizations could help users better understand the consequences of their choices. A more advanced public service would be to use XAI for risk assessment in the Defim DEFI protocols.
Mixing the XAI with blockchain technology could also be a powerful monitoring and chain handling detection tool. The AI is good for analyzing the models of sandwich attacks, MEV operations or washing trading. This could help find market abnormalities.
Some web projects are already trying to improve AI transparency. Singularitynet, for example, focuses on the appointment of the verification of AI processes. Another platform called Ocean Protocol follows the origins of the data, guaranteeing reliability and traceability.
Conclusion
At this point, this is only the beginning of the integration of blockchain and AI. Researchers are now exploring hybrid models that combine the integrity of blockchain, XAI clarity and bias detection tools in systems that can monitor and potentially correct.
But technology alone will not resolve this. He will also need attention to regulators, a meticulous examination of users and the humility of developers who build these systems. If the 2008 financial crisis has taught us something, it is that blind confidence in complex and centralized tools is dangerous.
In particular, Smart does not always mean fair. As a matter of traditional AI surfaces, users must also seek transparency in addition to efficiency.
