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Artificial intelligence has become a strong force in the ecosystem of finance, as it provides faster data -based visions that promise to improve investment, lending and risk management. One of the artificial intelligence consultants who allocate financial strategies for both companies and individuals to very advanced trading systems that make data -based decisions in MicroseConds, the AI financial sector has a large space for growth.
But there is one major problem: bias.
Despite the provision of speed, accuracy and what appears like objectivity, artificial intelligence systems have the same bias that the industry is trying to eliminate for decades. For example, according to the University of Lehigh, the large GPT-4 Turbo Models of Openai-simulates an intelligence mortgage consultant or decision-required Some of the population composition of applicants have 120 higher credit points than the white applicants to receive the same approval despite the same income, credit date, and debt levels.
This bias not only affects traditional financial markets, but also affects decentralized ecosystems and ecosystems for encryption. Take the platform for the Acting Market, for example. Since their data depends on the history of prices, news morale, or social trends, these platforms may sometimes exaggerate their reaction to abnormal cases – Crypto is full of black swan events such as Terra, FTX, or major penalties from organizers.
Thus, these prediction tools can become more than social trends or even gossip, which leads to bad signals and predictions.
Blockchain, xi to rescue
The restrictions and the dark nature of many artificial intelligence systems prevent them from becoming completely transparent and wondering. Some are even called black boxes because artificial intelligence models usually have slim or non -transparency.
It is worth noting that the decisions made by artificial intelligence tools within the encryption space are usually not explained – this makes it difficult for users to understand how to make decisions. The lack of uniform auditing protocols for artificial intelligence systems may also lead to inconsistent assessments and potential supervision of critical issues.
Integration of Blockchain technology with artificial intelligence, or XAI, for a short period, can address this problem by providing stability and transparency that comes with the notebook of the decentralized professor – improving specifically auditing methods, because auditors will have full access to basic system data and basic algorithms.
Xai models are already with increasing attention because they guarantee that the decision -making process is fair and moral in addition to being effective. Blockchain technology can complement XAI justice by creating fixed records of decisions of artificial intelligence, ensuring that every action can be followed and verified. This will enhance confidence and accountability.
Blockchains operates in an unreliable way. This does not mean that technology cannot be trusted, but it indicates that third parties or central authorities will not be required to confirm any decisions. Decentralization removes the need for a central entity to oversee operations, thanks to smart contracts that operate independently.
When the model changes or comes out of a decision, there can be no records of issuance and the control of issuance in confidence problems with most artificial intelligence platforms. Blockchain technology time stamps of records and data on a non -changing professor’s book.
Fico, a credit registration company, it has user Blockchain to register the decisions of the artificial intelligence model, so that the organizers can track how decisions such as credit approvals. The company was awarded the “Tech of the Future – Plockchain and Tokenisation” at London Banking Technology Awards last year.
From theory to practice
Blockchains and decentralized financing protocols have the opportunity to bake fairness, transparency and accountability in artificial intelligence models – which is struggling with traditional financial companies.
The combination of Xai and checking the series can convert how to make decisions and confidence in the Web3 ecosystem. For example, the use of Xai to explain the vote on the decentralized independent organizations can help user to better understand their options. The most advanced Xai risk evaluation tool will use the Defi protocols.
Xai mixing with Blockchain technology can also be a strong monitoring and discovery tool for the chain. Artificial intelligence is good in analyzing the patterns of sandwiches, MEV or trading. This can help find abnormal cases on the market.
Some Web3 projects are already trying to enhance the transparency of artificial intelligence. For example, singularitynet focuses on making artificial intelligence scrutinable. Another platform called the ocean protocol tracks data assets, ensuring the merit of confidence and tracking.
conclusion
At this stage, it’s just the beginning of Blockchain and AI integration. Researchers now explore mixed models that combine Blockchain safety, Xai clarity, and biases detection tools in systems that can monitor themselves and perhaps them.
But technology alone will not fit this. It will also need attention from the organizers, scrutiny of users, and humility from developers who build these systems. If the 2008 financial crisis teaches us anything, then this is that blind confidence in complex and central tools is dangerous.
It is worth noting that Smart does not always mean fair. Since the era of prevailing artificial intelligence surfaces, users must also search for transparency as well as efficiency.
