International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 08 | Aug 2024
p-ISSN: 2395-0072
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Unveiling Bias: Analyzing Artificial Intelligence and Machine Learning’s Impact on Fairness in the Criminal Justice System Sukanya Konatam1, Venkata Naga Murali Konatam2 1Senior Manager of Enterprise Data Governance and Data Science, IT Department, Vialto Partners, Texas, USA 2Data Architect, IT Department, Capital One, Texas, USA
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Abstract - This paper examines the integration of artificial
parole. [1] Addressing these biases is crucial to ensure fairness and justice in AI and ML applications in criminal justice. This involves scrutinizing the data used to train these models, implementing robust bias detection and mitigation techniques, and ensuring transparency and accountability in AI decision-making processes. [1] As we continue to integrate AI and ML into the criminal justice system, it is essential to remain vigilant about the potential for bias and to develop strategies to counteract it, ensuring these technologies contribute positively to the criminal justice system rather than reinforcing existing disparities. [1]
intelligence (AI) and machine learning (ML) in the criminal justice system, highlighting both the potential benefits and significant concerns related to bias. AI and ML are being employed to enhance various aspects of criminal justice, including crime prediction, tracking, and judicial decisionmaking. However, these technologies are susceptible to biases inherent in the historical data they rely on, which can perpetuate and amplify existing disparities within the justice system. The article delves into the historical context of AI advancements in criminal law, from early digitization efforts to the current deployment of sophisticated AI
2. EVOLUTION AND CURRENT APPLICATIONS OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN CRIMINAL JUSTICE
applications. It explores notable AI and ML technologies used in criminal justice, such as risk assessment tools, predictive policing algorithms, and facial recognition systems. The discussion emphasizes the ethical implications of AI bias, particularly its impact on marginalized communities. To address these issues, the article proposes various strategies for detecting and mitigating biases in AI/ML systems, including bias detection tools, data pre-processing techniques, and the importance of transparency and accountability. By scrutinizing these technologies and their applications, the article aims to contribute to the development of fairer and more equitable AI systems in criminal justice.
A.
The integration of Artificial Intelligence (AI) and Machine Learning (ML) into the criminal justice system has been a gradual process, marked by key advancements over the past few decades. [3] In the early 2000s, the digitization of court filings and processes set the stage for the initial adoption of AI-powered tools to assist in partial criminal court decisions. [4] This was followed by the emergence of online dispute resolution (ODR) as an alternative to traditional in-person court proceedings, further showcasing the potential of AI to transform the judicial landscape. [4] The 2010s saw a significant surge in the development and deployment of more sophisticated AI applications within the criminal justice domain. [3] Algorithmic risk assessment tools were introduced to inform bail, sentencing, and parole decisions, leveraging predictive analytics to assist human decisionmakers. [4] Jurisdictions such as the United States and the European Union began exploring the use of AI-powered digital tools to streamline various legal processes and support the work of judges and law enforcement. [4] More recently, in the late 2010s and early 2020s, the legal sector has witnessed the emergence of AI-driven solutions that can potentially automate certain adjudicative functions, raising complex ethical and legal questions about the role of technology in judicial decision-making. [2] As these advancements continue to unfold, the criminal justice system is grappling with the challenges and opportunities presented by the integration of AI and ML, seeking to harness the benefits while ensuring the protection of fundamental rights and the rule of law. [5]
Key Words: Bias in Judicial AI, Ethical AI in Criminal Law, Bias in Artificial Intelligence, Machine Learning Accountability, ML Bias in Criminal Justice
1.INTRODUCTION - ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN THE CRIMINAL JUSTICE SYSTEM Artificial intelligence (AI) and machine learning (ML) algorithms are increasingly being adopted within the criminal justice system to tackle various challenges, such as predicting and tracking crimes and criminals and assisting in criminal court proceedings. [1] These technologies hold the potential to enhance efficiency and effectiveness in crime prevention and investigation efforts. However, the use of AI and ML in criminal justice also raises significant concerns regarding potential biases and privacy infringements. These models are built on historical data, which often reflect and perpetuate existing societal biases. [1] Biases in AI and ML systems can influence various stages of the criminal justice process, from arrest and bail decisions to sentencing and
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