Author: Zen and the Art of Computer Programming
1.1 The role of AI in justice
In order to promote the construction of China's fair judicial system and build a more credible, fair and efficient institutional environment, in 2019, Lu Huiming, deputy director of the Central Senior Law Firm, published a discourse on "Building a National Free Rule of Law in China", proposing to use the system The most equitable judicial system in the world has been established with modernized legal means. With the development of artificial intelligence technology, the application of image recognition and natural language processing based on deep learning has gradually become a sharp weapon to solve judicial problems, especially in legal issues such as difficult judgments, late judgments, and weak defenses. achieved certain results.
1.2 The background and significance of this study
1.2.1 Definition of judicial impartiality
Judicial fairness refers to the fact that judges try cases based on the evidence provided by the law and the courts, can make accurate, complete and correct judgments, and provide fair judgments for the defendant's claims and actions. Judicial impartiality is the main evaluation standard to measure whether a judiciary reaches the highest level of fairness and justice.
In recent years, with the development of technologies such as computer vision and natural language processing, traditional methods have been unable to completely solve judicial problems, and even lag seriously behind. AI can help judges to conduct legal investigations, identify criminal suspects, convict and send sentences faster, more accurately and more reliably. Through technical means, disadvantaged groups, marginalized groups, and people of different cultures are included in the jurisdiction, so that poor areas, rural areas, and immigrant areas are treated equally.
1.2.2 Limitations of Current AI Technology
In practical applications, due to the large differences in the requirements of different scenarios, characteristics and tasks, AI technology still has some shortcomings. For example, there is a lack of objective standards for the objectivity and fairness of judgment results; the legal rules of low-level power agencies cannot be automatically converted to AI models, resulting in biased judgment results; at the same time, the ability to identify individual "discrimination" events also has certain limitation. Therefore, this paper puts forward three suggestions in response to the above problems, hoping to further improve the impact of AI technology on judicial companies.