AI & Racial Equity: Understanding Sentiment Analysis Artificial Intelligence, Data Security, and Systemic Theory in Criminal Justice Systems

TL;DR

This paper examines how artificial intelligence, especially natural language processing, impacts racial justice in criminal systems, highlighting risks of bias and the need for stricter regulations and auditing to prevent systemic injustice.

Contribution

It provides an analysis of AI's role in racial disparities within criminal justice, emphasizing the importance of policy and oversight to mitigate biases and systemic risks.

Findings

AI risk assessment tools exhibit racially disparate outcomes

Legal and policy frameworks are insufficient to regulate AI in criminal justice

Enhanced auditing and regulation are necessary to prevent racial injustice

Abstract

Various forms of implications of artificial intelligence that either exacerbate or decrease racial systemic injustice have been explored in this applied research endeavor. Taking each thematic area of identifying, analyzing, and debating an systemic issue have been leveraged in investigating merits and drawbacks of using algorithms to automate human decision making in racially sensitive environments. It has been asserted through the analysis of historical systemic patterns, implicit biases, existing algorithmic risks, and legal implications that natural language processing based AI, such as risk assessment tools, have racially disparate outcomes. It is concluded that more litigative policies are needed to regulate and restrict how internal government institutions and corporations utilize algorithms, privacy and security risks, and auditing requirements in order to diverge from racially injustice outcomes and practices of the past.