The world’s largest democracy, India, is struggling with monolithic law enforcement and judicial efficiency challenges. Having over 5 crore (50 million) pending cases across all courts and police manpower ratio of roughly 153 officers per 100,000 population, need for a technology revolution in public safety and justice delivery has never been so acute. Emerging technologies such as Machine Learning (ML) and Deep Learning (DL) are now redesigning conventional institutions all over the world—and India is gradually unlocking their potential for transformation in its police and judiciary to remove inefficiencies, facilitate quicker decision-making, minimize human prejudice, and build confidence among people.
In India’s policing system, ML and DL have been implemented to their full capacity in crime forecasting, facial recognition, and surveillance examination. One of the most powerful applications is predictive policing. Decision tree, random forests, and neural network-based machine learning algorithms operate on historical crime data to forecast possible hotspots for crime by analyzing temporal and spatial data. Delhi Police, for example, have piloted a predictive policing platform in 2021 based on historical incident reports to forecast burglars and thefts in densely populated localities. Preliminary reports showed 15–20% decrease in response time and 9% decrease in property crime in pilot locations.
Face recognition technology, founded on convolutional neural network (CNN)–based deep learning model, also found favor with Indian police. National Crime Records Bureau (NCRB) implemented the Automated Facial Recognition System (AFRS) in 2020. Through 2023, the system recognized over 6,500 missing children and 3,000 unidentified human remains. But facial recognition also brings to the table necessary privacy, accuracy, and bias concerns, as the system’s accuracy would have to hinge on gender and ethnic groups. Civil society groups have called for regulatory intervention and data protection to avoid abuse on ethical grounds.
Besides surveillance, deep learning finds use in natural language processing (NLP) operations like First Information Report (FIR) processing and criminal complaint pattern identification. Artificial intelligence applications developed by institutions and start-ups like IITs help in classifying FIRs based on the type of crime, extracting valuable named entities (like suspects and locations), and linking cases across jurisdictions. AI-facilitated FIR analysis in Uttar Pradesh and Maharashtra reduced document time by over 40% and crime linkage identification to the same degree as well. Not only does it end up saving time, but it also maximizes evidence-based policing.
Machine learning also becomes the center of cybercrime detection. With over 1.65 lakh cybercrime in 2023 (24% increase from 2022), Indian police are using ML algorithms to detect suspicious online transactions, phishing emails, and cyberbullying. AI-powered solutions run by Telangana Cyber Police have been successful in detecting false websites and blocking hundreds of trap numbers. Through the help of network analysis and behavioral intelligence, AI technology mitigated online fraud losses by up to ₹85 crores independently in 2022 alone.
To the judiciary, ML and DL are finding their way into case management facilitation, legal research, and decision support. India has one of the world’s largest judicial backlogs with 3–10 years of average case lifecycles depending upon the hierarchy of courts. To play this role, such as the Supreme Court Portal for Facilitation of Court Efficiency (SUPACE) recently initiated by the Supreme Court of India in 2021, utilize ML to support judges’ research work by extracting case documents, determining precedents, and deducing applicable legal principles. SUPACE does not determine cases but significantly reduces judges’ and clerks’ clerking workload. Early results suggest review time for papers cut by over 30% on some High Court benches where the pilot was tested.
Legal analytical platforms such as CaseMine, Indian Kanoon, and Manupatra more and more utilize ML algorithms to perform case clustering, relevance ranking, and predictive case outcome analysis. The platforms enable lawyers and judges to scrape judgments, precedents trends review, and derive insights on judicial behavior. The Vidhi Centre for Legal Policy study of 2022 assumed that courts using AI-based search tool software saw a response time of 22% reduction in legal research, thereby speeding up the disposal of cases. Machine learning has also been utilized in bail and sentence decisions, which is a crucial area in Indian criminal law.
While judicial discretion is never impossible to eradicate, probabilistic recidivism or risk-of-flight predictions or pattern-based sentencing suggestions can be generated by algorithms trained from large databases. Pilot programs in several Indian courts have experimented with AI-based bail determination with results conforming with algorithmic recommendation and judicial orders more than 75% of the time, including flagging cases of inconsistency or suspected bias. Deep learning is applied in prisons to track the movement of prisoners through video analytics and biometric sensors. AI-enabled monitoring devices are capable of identifying violent postures, gang violence, or attempts to escape, thereby facilitating real-time intervention. They are being piloted in Tihar Jail and other Indian prisons presently, with initial reports showing a 28% decrease in response time for prisoner fights and smuggling of contraband.
As these advantages compound, India’s police and judiciary uses of ML and DL are fraught with operational and ethical issues.
Highest among them is data quality and bias. Since AI learns from existing data, system biases—such as over-policing the minority or flip-flopping by judges—get learned and imitated unwittingly. A report in 2023 by the Internet Freedom Foundation had already established algorithmic bias in facial recognition that performed worse against women and tribal communities. AI, unless it is empowered with strong governance and transparency infrastructure, can entrench entrenched biases. Then there is the issue of data privacy and protection. Personal data in police and judiciary databases is usually poorly protected. The Personal Data Protection Bill that is before Parliament will have an essential role to play in determining how public security and justice AI systems treat information responsibly.
Lastly, there are not many professionals with enough experience in the area of AI who are employed in public departments, and that is a constraint.
There is minimal training for the police personnel and court officials in data science, and none in terms of infrastructure as regards cloud capacity and computational power. In order to bridge this gap, state police academies and the National Judicial Academy have begun incorporating training in AI into their syllabus, while the Ministry of Electronics and Information Technology is also funding research in AI under its National AI Mission. India’s future is in creating explainable AI (XAI) architectures, algorithmic audits, and human-in-the-loop decision-making to facilitate accountability and trust. There must be a convergence of the government, industry, and academia to make sure that scalable, ethical, and inclusive AI systems are formed.
In conclusion, the incorporation of machine learning and deep learning into India’s policing and judicial systems can potentially enhance transparency, efficiency, and justice in them.
Though challenging, through right usage of AI rules and regulations, advantages may be achieved that will give us a secure country and more prompt, equitable system of justice. Due to virtue of innovation mixed with approach of rights, India can make the world imitate as the model for organizing the destiny of justice through artificial intelligence.
Prepared by
Jagadish Sripelli,
Assistant Professor, School of Computer Science and Artificial Intelligence,
SR UNIVERSITY, Warangal
jagadish.sripelli@gmail.com
