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Artificial Intelligence (AI) has rapidly influenced various parts of society, including the criminal justice system. While AI offers the potential to streamline operations and enhance fairness, it has also sparked concerns about transparency, bias, and accountability.
Grossman, M. R. (2023, April 28). Artificial Justice: The quandary of
AI in the Courtroom. Judicature | the Scholarly Journal About the
Judiciary. https://judicature.duke.edu/articles/artificial-justice-the-
quandary-of-ai-in-the-courtroom/
The National Institute of Justice (NIJ) highlights the profound impact of AI on the criminal justice system, with a focus on improving efficiency and supporting law enforcement agencies. AI-driven predictive policing systems have proven invaluable for optimizing resource allocation and crime prevention strategies. Through the analysis of historical crime data and the identification of underlying patterns, these systems empower law enforcement to distribute resources more effectively to regions with higher crime rates, resulting in faster response times and the potential reduction of criminal activities.
However, as underscored by research from the Social Science Research Network (SSRN), the integration of AI into predictive policing is not without controversy. This controversy draws attention to concerns about inadvertently perpetuating established biases in law enforcement, as AI algorithms may unintentionally uphold historical patterns of discrimination. This has led to significant analysis of the methods used to train AI systems and their ability to remain objective.
The lack of transparency in AI systems within the criminal justice sphere carries extensive consequences. It can demolish public trust in the criminal justice system, give rise to concerns about fairness and accountability, and limit defendants' ability to review AI algorithm-based decisions, potentially violating their rights. The demand for transparency in AI systems used in the criminal justice sector is growing increasingly urgent. Transparency not only ensures accountability but also facilitates the detection and correction of biases ingrained within AI algorithms.
SSRN proposes a practical solution by suggesting a framework for monitoring AI systems in the criminal justice domain. This framework advocates for the establishment of third-party auditing mechanisms to guarantee the fairness and impartiality of AI algorithms. By involving external experts in the evaluation of AI systems, it becomes possible to mitigate the risks associated with concealed biases.
For further insight on the risks of AI in criminal justice, specifically in terms of predictive algorithms:
Sources:
Hamilton, M. (n.d.). A ‘black box’ AI system has been influencing criminal justice decisions for over two decades – it’s time to open it up. The Conversation. https://theconversation.com/a-black-box-ai-system-has-been-influencing-criminal-justice-decisions-for-over-two-decades-its-time-to-open-it-up-200594
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