Description
Abstract:
The rapid increase in the use of artificial intelligence has raised concerns regarding the inadvertent reinforcement of societal biases. As artificial intelligence is being used to aid criminal justice systems, biased outcomes are a cause for concern. This study delves into the pervasive issue of implicit bias embedded within AI-generated content and its impact on criminal justice. Our research aims to comprehensively analyze these implicit biases, shedding light on their origins, manifestations, and potential consequences. To achieve this objective, a qualitative approach is employed. The primary data collection involves the extraction of AI-generated images from various platforms. This expansive dataset is then subjected to content analysis to identify patterns of bias. In addition to primary data, an extensive review of existing literature on AI bias is conducted to enrich the study with a broader context. Secondary data sources include scholarly articles, industry reports, and policy documents, offering valuable insights into the ethical dimensions of AI development. The anticipated outcomes of this study are twofold. First, it aims to provide a comprehensive mapping of implicit biases present in AI-generated content, offering a nuanced understanding of their societal implications. Second, the research seeks to propose mitigation strategies and ethical guidelines for developers and policymakers to address and rectify these biases. Ultimately, the findings hold the potential to inform industry practices, guide policy formulations, and foster a more equitable and unbiased landscape in the realm of AI-generated content.
Keywords: Artificial Intelligence, Restorative Justice, Implicit Bias, Criminal Justice System, Justice Delivery
