Social media platforms may provide potential space for discourses that contain hate speech, and even worse, can act as a propagation mechanism for hate crimes. The FBI's Uniform Crime Reporting (UCR) Program collects hate crime data and releases statistic report yearly. These statistics provide information in determining national hate crime trends. The statistics can also provide valuable holistic and strategic insight for law enforcement agencies or justify lawmakers for specific legislation. However, the reports are mostly released next year and lag behind many immediate needs. Recent research mainly focuses on hate speech detection in social media text or empirical studies on the impact of a confirmed crime. This paper proposes a framework that first utilizes text mining techniques to extract hate crime events from New York Times news, then uses the results to facilitate predicting American national-level and state-level hate crime trends. Experimental results show that our method can significantly enhance the prediction performance compared with time series or regression methods without event-related factors. Our framework broadens the methods of national-level and state-level hate crime trends prediction.

For many years, AI/ML has been used to establish the identity of perpetrators, the perpetrators' whereabouts at the time of a criminal act and their actions and whereabouts prior to and following a criminal act. By hand, these are arduous tasks but AI categorization sifting through massive amounts of visual data along with ML behavior scripts AI/ML algorithms can eliminate human errors especially in witness identification and therefore increasing arrest accuracy. "Predictive policing" is the practice of identifying the date, times and locations where specific crimes are most likely to occur, then scheduling officers to patrol those areas in hopes of preventing crimes from taking place, therefore keeping neighborhoods safer. After much research and input from major police departments in cooperation with software suppliers, predictive analytic models have been continuously refined. A profile matrix can be constructed from a database containing known associates, possible DNA found at the scene, gunshot detection, etc.

Police in Vancouver, British Columbia are cracking down on burglary with a machine learning solution that uses an algorithm to deconstruct crime patterns. Through spatial analytics, police are able to predict where residential break-and-enters will occur and place police patrols accordingly. The department first tried this technology with a pilot test that reduced burglary by more than 20% month over month. Now they are making the approach common practice. "Every 28 days, our management reviews crime trends, crime clustering, and crime issues across the city," said Ryan Prox, Special Constable in Charge of Crime Analytics Advisory and Development Unit, Vancouver Police.