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Deep Learning-Based Intelligent Robot in Sentencing.

Xuan Chen1

  • 1Department of Social Work, School of Law and Politics, Zhejiang Sci-Tech University, Hangzhou, China.

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Summary
This summary is machine-generated.

Deep learning artificial intelligence (AI) significantly reduces legal sentencing trial periods for various cases. This AI-assisted sentencing shows over 92% accuracy, offering a valuable tool for judicial innovation.

Keywords:
artificial intelligencedeep learninglawrestricted boltzmann deep learning modelsentencing

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Area of Science:

  • Computer Science
  • Law
  • Artificial Intelligence

Background:

  • Judicial systems face challenges in efficient and consistent sentencing.
  • The integration of artificial intelligence (AI) offers potential solutions for streamlining legal processes.

Purpose of the Study:

  • To explore the application of deep learning AI in legal sentencing.
  • To assess the impact of an AI-driven intelligent robot system on case trial durations and accuracy.

Main Methods:

  • Introduction of sentencing principles and deep learning concepts.
  • Proposal of a deep learning model for an intelligent robot in judicial trials.
  • Integration of the deep learning model into an intelligent robot system for case sentencing assistance.

Main Results:

  • The AI model, specifically a restricted Boltzmann deep learning model, reduced trial periods across multiple case types (e.g., traffic accidents, copyright infringement).
  • Average trial periods decreased significantly, with accuracy rates exceeding 92%.
  • The AI system effectively shortened trial periods for most cases, excluding theft cases.

Conclusions:

  • Deep learning-based AI, integrated into intelligent robot systems, demonstrates significant potential to assist in legal sentencing.
  • The AI system can effectively reduce case trial durations, providing a theoretical basis for future AI applications in the judicial system.