Using Artificial Intelligence in Policing

The us of artificial Intelligence in policing represents a potentially powerful advance in enhancing public safety if it's implemented responsibly and ethically. AI technologies such as facial recognition, predictive analytics and advanced surveillance systems can enhance the effectiveness of policing when it comes to crime prevention, investigation and community engagement. In the future, to ensure that individual rights are protected and biases are avoided, advances in the effectiveness of controlling through the use of AI must be tempered by strict ethical considerations. The ethical use of AI in policing can improve crime control effectiveness and also build public trust and legitimacy. In the digital era in which we now live of digital expansion and society's evolution it is essential that policing use AI in a way that adheres to ethical principles, respects constitutional frameworks and promotes transparency.

Here we examine the rapidly expanding field of AI in policing. We will examine its current practical uses and its rapidly expanding breadth of potential use in the future. We will also discuss how interested stakeholders can analyze the extent of AI’s use in their local circumstances and the tools that can be utilized to gain a collective understanding of its future use and implications – good and not so good – for policing’s use of AI.

AI and Post-Conviction Review: How Police Departments Can Restore Confidence
Philip Lukens, AI, post conviction analysis Chief Philip Lukens (ret.) Philip Lukens, AI, post conviction analysis Chief Philip Lukens (ret.)

AI and Post-Conviction Review: How Police Departments Can Restore Confidence

Post-conviction analysis is the process of reviewing past cases and evidence to identify and correct wrongful convictions, which are a serious and pervasive problem in the criminal justice system. AI driven post-conviction analysis can enhance public trust in the justice system, reduce the costs and harms of incarceration, prevent future miscarriages of justice and train large language models. However, post-conviction analysis also faces many challenges and barriers, such as lack of resources, data, standards, transparency, cooperation, incentives, and public awareness.

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