Part 1: Low-Code & Expert Systems — An Intellectual Workout for Lawyers
Here, technology serves as a tool for codifying professional expertise. Using a low-code platform, a lawyer formalizes their knowledge, logic, and understanding of case law into clear algorithms. The result is an automated advisory system that generates documents and provides precise answers.
Key Benefits for the Creator:
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Professional Growth. The process of building algorithms demands deep analysis of legislation, case law, and their systematization. This reveals regulatory gaps and hones legal thinking.
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Control & Relevance. The lawyer has full ownership of the system. Changes in law require thoughtful analysis and precise adjustments to algorithms, keeping their expertise sharp.
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Scaling Value. The created system assists thousands of users with routine matters, but its core value lies in the development process for the creator themselves.
Part 2: Neural Networks & ML — The Risk of Substituting Thought
This approach is based on training models on vast datasets (court rulings, contracts). However, in a dynamic legal environment, it faces critical issues:
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Latency. A model trained on outdated practice cannot adequately respond to legal changes until it is retrained on a massive new dataset.
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The "Black Box" & Hallucinations. It's impossible to trace the model's reasoning. This creates the risk of citations to non-existent norms and precedents, requiring constant lawyer verification.
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Skill Degradation. Using a neural network as a "crutch," a lawyer becomes a passive consumer of information. The incentive for deep analysis, synthesis, and independent conclusion-making diminishes as the brain chooses the easiest path.
A Dangerous Loop: When AI Trains AI
A special risk emerges if neural networks are implemented in government agencies and courts. If legal practice begins to be shaped by AI, and new models are trained on this AI-generated data (without proper labeling), it will lead to error accumulation and the "intellectual degeneration" of systems.
Conclusions & Recommendations for the Legal Community
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Education: Law schools should focus on teaching students to build algorithmic expert systems (using low-code), not just use neural networks.
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Practice: Neural networks are best suited for applied, non-creative tasks: rapid document search, initial classification, pattern recognition. Expert systems are for tasks requiring precision, verifiability, and expertise development.
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Philosophy: A lawyer must remain an architect of solutions, not an operator of a "magic box." Low-code is a technology that augments the lawyer, not one that replaces their critical thinking.