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AI Use Cases

A collection of over 250 uses for artificial intelligence

A continually updated list exploring how different types of AI are used across various industries and AI disciplines,including generative AI use cases, banking AI use cases, AI use cases in healthcare, AI use cases in government, AI use cases in insurance, and more

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Predictive policing

Introduction

The use of ML and AI has also revolutionized many industries including law enforcement and that is why predictive policing has emerged. Predictive policing is a strategy that involves the use of technology to determine the future occurrence of crimes. This approach applies ML models to understand criminal patterns and to identify possible crime locations therefore, making it possible for the police to respond to crimes preventively. In other words, predictive policing involves the application of predictive analytics to improve on the safety of the public as well as the efficiency of the operations.

Challenges

There are however some challenges that beset the use of predictive policing. The first challenge is the reliability of the predictions whose accuracy is dependent on the quality of data input into the system. This means that wrong or skewed data can produce wrong results thus aggravating the problems of discrimination and bias within the justice system. In addition, there is the issue of algorithmic transparency where AI algorithms can be black boxes making it difficult to understand how certain decisions are being made. Also, there is the issue of privacy since the use of AI in law enforcement involves collecting a lot of data which may be violating individuals’ rights.

AI Solutions

In order to tackle these challenges, several AI solutions have been put forward. There are advanced ML algorithms in development to mitigate bias and enhance the predictive capabilities. For example, there are fairness by design algorithms that are meant to deliver equal results in the predictions made. In the area of transparency, there is a growing field of XAI where new models of AI are being developed to make the decisions of the AI more comprehensible. Also, to mitigate the issue of privacy while seeking to acquire data, there are privacy-preserving AI techniques like differential privacy. Furthermore, the solutions are being designed to incorporate human oversight into AI systems in order to maintain accountability.

Benefits

Predictive policing powered by AI offers several benefits. It can help law enforcement agencies optimize resource allocation, as officers can be deployed to areas where crime is predicted to occur. This proactive approach can also deter crime, as visible police presence may dissuade potential criminals. Furthermore, predictive policing can aid in crime investigation, as patterns identified by AI can help uncover leads. Lastly, the use of AI can speed up tedious administrative tasks, freeing up officers to engage in more crucial tasks.

Return on Investment

The ROI of AI in the area of predictive policing can be quite impressive. Let’s start by saying that the increase in the efficiency of proactive policing can contribute to the prevention of crime, which in turn will yield social savings. Also, the optimization of resource allocation is a way to reduce the operational costs. In addition, the increase in the speed of administrative processes will also lead to the enhancement of time, which will also contribute to the improvement of the overall performance. It is difficult to determine the exact ROI as it would depend on the situation and how AI was integrated, but there is a great potential for cost savings in the future.

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