JOURNAL ARTICLE

Predictive Policing: A Fairness-aware Approach.

  • Published In: International Journal on Artificial Intelligence Tools, 2024, v. 33, n. 3. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Downey, Ava; Islam, Sheikh Rabiul; Sarker, Md Kamruzzman 3 of 3

Abstract

As Artificial Intelligence (AI) systems become increasingly embedded in our daily lives, it is of utmost importance to ensure that they are both fair and reliable. Regrettably, this is not always the case for predictive policing systems, as evidence shows biases based on age, race, and sex, leading to wrongful identifications of individuals as potential criminals. Given the existing criticism of the system's unjust treatment of minority groups, it becomes essential to address and mitigate this concerning trend. This study delved into the infusion of domain knowledge in the predictive policing system, aiming to minimize prevailing fairness issues. The experimental results indicate a considerable increase in fairness across all metrics for all protected classes, thus fostering greater trust in the predictive policing system by reducing the unfair treatment of individuals. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:International Journal on Artificial Intelligence Tools. 2024/05, Vol. 33, Issue 3, p1
  • Document Type:Article
  • Subject Area:Social Sciences and Humanities
  • Publication Date:2024
  • ISSN:0218-2130
  • DOI:10.1142/S0218213024600054
  • Accession Number:176779977
  • Copyright Statement:Copyright of International Journal on Artificial Intelligence Tools is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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