India is a land of “Unity in diversity” with an official constitution characterized by the principles of “Justice, Liberty, Equality, and Fraternity”. As the century is progressing, we are becoming more tech prone, as our all needs are being catered to by the realm of it. In a similar strain, a great amount of automation is brought about by data analytics, machine learning and artificial intelligence systems are taking over the entire system of the judiciary and legal recourse.
With the advent of technological innovations like the Artificial Intelligence System, the risk of criminal activities as well as the operation of the criminal justice system is technically becoming sophisticated day by day. The challenges put forth by the sophisticated systems of Artificial Intelligence makes us revisit the common areas of explanation of the grounds of judgment, enquiring as to whether such grounds are transparent, what a fair trial is, and as to how the artificial intelligence systems will guarantee the enforcement of the law to the sections who are literally deprived of it.
The technical sophistication of the new AI systems used in decision-making processes in criminal justice settings often leads to a ‘black box’ effect. Intermediate phases in the process of reaching a decision are by definition, hidden from human oversight due to the technical complexity involved. For instance, multiple areas of applied machine learning show how new methods of unsupervised learning or active learning operate in a way that avoids human intervention. In the active approaches of machine learning used for natural language processing. For instance, the learning algorithm accesses a large corpus of unlabelled samples and, in a series of iterations, the algorithm selects some unlabelled samples and asks the human annotator for appropriate labels. The approach is called active, as the algorithm decides what samples should be annotated by the human based on its current hypothesis. The core idea of active machine learning is to eliminate humans from the equation. Moreover, artificial neural networks (hereafter ‘ANN’) learn to perform tasks by considering examples, generally without being programmed with task-specific rules. As such, artificial neural networks can be extremely useful in multiple areas, such as computer vision, natural language processing, geoscience for ocean modeling, or in cybersecurity for identifying and discriminating between legitimate activities and malicious ones. They do not demand labeled samples, e.g., in order to recognize cats in images or pedestrians in traffic, but can generate knowledge about what a cat looks like on their own. The operations in machine learning approaches are not transparent even for the researchers that built the systems and while this may not be problematic in many areas of applied machine learning, as the examples below show, AI systems must be transparent when used in judicial settings, where the explainability of decisions and the transparency of the reasoning are of significant—even civilizational—value. A decision-making process that lacks transparency and comprehensibility is not considered legitimate and non-autocratic. Due to the inherently opaque nature of these AI systems, the new tools used in criminal justice settings may thus be at variance with fundamental liberties.
Therefore, we are clearly informed regarding the bright side as well as the drawbacks of the judiciary system which is being taken over by the artificial intelligence systems slowly.