The Impact of Artificial Intelligence and Data Mining on Crime Prevention: Opportunities and Challenges
Subject Areas : Law and new technologiesSeyed Yaser Shujaei Langari 1 *
1 - Master's Degree in Criminal Law and Criminology, Licensed Attorney at Law
Keywords: Artificial Intelligence, Data Mining, Crime Prevention, Criminological Analysis, Privacy.,
Abstract :
With the emergence of new technologies in various aspects of social and economic life, crime prevention, as a key area in the field of law and criminology, has also been influenced by these developments. In this context, Artificial Intelligence (AI) and Data Mining have become advanced tools that enable the analysis of large-scale data and the identification of criminal patterns. These technologies, through complex machine learning algorithms, can process enormous amounts of data in a very short time and offer predictions based on statistical analyses of crimes, identify suspicious behaviors, and even simulate crimes. Recent advancements in AI and data mining have led to significant transformations in the way crime prevention is approached. One important application of these technologies is their use in predicting and simulating crime occurrences in specific areas or based on behavioral patterns. Specifically, these technologies help judicial and law enforcement authorities identify areas more prone to crime and adopt more effective preventive measures based on data analysis. However, the use of these technologies comes with various legal and ethical challenges. One of the most significant concerns is privacy, as the processing of personal data and smart surveillance can lead to violations of individual rights and personal freedoms. Additionally, algorithmic biases may result in unfair and discriminatory decisions against certain social, racial, or gender groups. Alongside these issues, the criminal liability of AI decisions is also a complex legal matter. In the event of errors by intelligent systems, it must be determined who or which entity is responsible for these mistakes.
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