THE IMPACT OF ARTIFICIAL INTELLIGENCE ON SCIENTIFIC RESEARCH IN THE FIELD OF LAW

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MEKKAOUI NAIMA , CHENINE SANA

Abstract

In the context of the rapidly evolving technological revolution, the relationship between scientific legal research and artificial intelligence (AI) is increasingly intertwined, with both fields complementing and influencing each other. Specialized AI systems used in legal research have sparked a true revolution in the production, analysis, and interpretation of legal knowledge. These systems have enhanced the quality and efficiency of legal research and analysis through large language models, machine learning systems, and big data analysis. As a result, they have significantly improved the process of producing and processing legal knowledge with greater speed and efficiency compared to traditional methods by analyzing vast amounts of legal information. This advancement has also led to the emergence of what is known as Legal AI, which seeks to model legal reasoning. All of these developments have had a direct impact on legal scientific research in terms of reliability and the ethical dimensions of legal scientific inquiry.

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The European Parliament, in its 2020 framework initiative on the civil liability regime for artificial intelligence, which includes specific recommendations regarding this liability system, did not focus on defining artificial intelligence itself but rather on defining AI systems. These are defined as: "A system—whether software-based or embedded in physical devices—that exhibits behavior mimicking intelligence, particularly by collecting and processing data, analyzing and interpreting its environment, and interacting within it, with a certain degree of autonomy, aiming to achieve specific objectives."

The first observation that can be drawn from this definition is that the European legislator clearly distinguishes between artificial intelligence as a technical and philosophical concept and AI systems. A reader of the report and its various annexes can conclude that the European legislator concentrated on the idea of AI systems while entirely overlooking artificial intelligence itself. Therefore, the term used in legal dealings with this technology has now become "AI systems" rather than "intelligence."

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There are two main perspectives on the meaning of "artificial" in the context of artificial intelligence. Critics argue that artificial intelligence is merely an illusion of intelligence. They consider these machines to be complex devices that mimic human thinking but do not actually think. On the other hand, proponents believe that AI can genuinely think, even if differently from human thinking. They compare it to how a car moves differently from a rabbit, yet both still move.

Supporters of the Illusion of Thinking Theory:They claim that AI only appears to think. For example, when we see a robot or computer making decisions, it is simply following programmed instructions and is not truly thinking like a human. This is similar to how a puppet may seem to dance but is actually controlled by strings.

Supporters of the Genuine Thinking Theory:They argue that once machines are built and programmed, they can perform tasks that seem like thinking. Just as a car can drive and an airplane can fly, AI can think in its own way. This means that even if AI's thinking differs from human thinking, it is still real.Do we need to prove that AI truly thinks? For instance, we don't need to prove that a lightbulb gives light; we just see that it does. Similarly, if AI shows signs of thinking, why not accept that it is intelligent? This is a complex issue because thinking is not as easy to observe as light or movement. The challenge with AI is that thinking is not something we can easily observe. Unlike a bright lightbulb, we cannot see the thoughts happening inside the machine. This makes it difficult to determine whether AI is truly intelligent or merely pretending to be.

In short, the debate over AI revolves around whether it truly thinks or just appears to think. The question of whether AI is intelligent remains open because thinking is not as visible as other processes. Therefore, the debate continues regarding the nature of AI intelligence.

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Big data refers to extremely large, complex, and voluminous datasets that traditional data processing systems cannot efficiently manage. These can include structured, semi-structured, and unstructured data generated or retrieved from various sources, such as social media, e-commerce platforms, sensors, government data, financial data, telecommunications, gaming industries, system logs, and software. Structured data is information organized in a specific, predefined format, like tables, spreadsheets, and databases.Different types of data can enhance the inclusivity and accuracy of data analysis. The availability of vast amounts of data has allowed AI algorithms to learn and make more accurate predictions. By using big data, AI models can be trained on larger datasets and make more informed and reliable predictions. As a result, managing, processing, and extracting insights from big data often require specialized tools and techniques, such as data storage, machine learning, and distributed computing. However, it is not just the quantity of data that matters, but what organizations do with it. Big data can be analyzed to gain insights that lead to better strategic decisions and actions. For more details, see:Chan, Cecilia Ka Yuk, and Tom Colloton. Generative AI in Higher Education: The ChatGPT Effect. Taylor & Francis, 2024, p. 3.

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