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Government CIO Outlook | Monday, October 26, 2020
The criminal justice system uses artificial intelligence technologies to decrease human intervention from the equation.
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FREMONT, CA: The automation introduced by artificial intelligence, big data analytics, and machine learning systems has started to challenge the law system to reconsider criminal justice's fundamental questions. Several strategies have been replaced by automation in the criminal justice domain.
With the development of artificial intelligence, big data analytics, and machine learning, evaluating the risk of crime and the operation of the criminal justice systems are becoming technologically advanced. Many people still disagree whether such technologies will be a solution for the criminal justice systems, like decreasing case backlogs, or it will intensify the social divisions and compromise fundamental liberties. It can become difficult for such people to agree that these sophisticated technologies might have significant consequences in the criminal justice systems. The automation introduced by AI systems have challenged the justice system to reconsider the primary questions of criminal justice like,
When is the procedure of embracing a judicial decision transparent?
[vendor_logo_first]• What is the meaning of explanation of the grounds of a judgment?
• Who should be accountable for (semi) automated decisions, and how should the responsibility be assigned in the chain of actors when the final decision is provided using AI?
• Is the due process of law denied to the accused when AI systems are utilized at some criminal procedure stage?
• What is a fair trial?
The technical evolution with the help of the AI system utilized in the decision-making processes in criminal justice can lead to a black-box effect. The transitional phases in reaching a decision are concealed from the human eye because of the technical intricacy related to it. For example, numerous fields of applied machine learning demonstrate how new techniques of active learning operate in a process to avoid human intervention. The dynamic approach of machine learning utilized for natural language processing, like the learning algorithm, accesses a massive quality of unlabelled samples. During repetitions, the algorithms pick some of the unlabelled samples and request the human annotator for accurate labels.
The method is known as active because it decides which samples must be annotated by the human based on its present hypothesis. The primary idea of active machine learning is to reduce human intervention from the equation. The artificial neural networks (ANN) can learn to complete tasks with the help of examples. The artificial neural network can also be beneficial in several areas like natural language processing, cybersecurity for identifying and discriminating between legitimate activities and malicious ones, computer vision, and geoscience for ocean modeling.
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