AI Machine learning of artificial intelligence systems with acts of justice: Forecasting and ways to solution

In the modern world, characterized by rapid technological development, artificial intelligence (AI) penetrates into all areas of human activity, including justice. One of the promising areas of AI application is machine learning based on acts of justice. This technology allows AI systems to analyze huge volumes of court decisions, identify patterns, and make predictions. However, the implementation of such systems is associated with a number of problems that must be taken into account and solved. The main emphasis is placed on the use of AI for analyzing big data, automating routine processes, and minimizing subjective errors in decision-making. Successful examples of integrating digital tools into the judicial systems of other countries are considered, which emphasizes the relevance of introducing such approaches in Kazakhstan. The results show that the use of AI and digital tools helps to reduce the number of canceled decisions, improve the predictability of legal outcomes, and increase citizens' trust in the judicial system. The study used a combination of mathematical, statistical, and machine learning methods to develop an AI-based judicial decision-making model to improve the model and expand data sources to enhance accuracy and applicability. The model represents a comprehensive approach to judicial decision prediction, combining powerful algorithms (logistic regression, XGBoost, LSTM) and fairness mechanisms (SPD, anomalies). The implementation of such solutions can not only improve the accuracy of judicial predictions but also provide transparency and control over possible AI bias. The study highlights the potential of artificial intelligence to improve judicial decision-making by increasing efficiency, reducing bias, and ensuring consistency in judicial decisions.