JOURNAL ARTICLE

Showcase: A Data-Driven Dashboard for Federal Criminal Sentencing.

  • Published In: Journal of the Association for Information Systems, 2023, v. 24, n. 6. P. 1479 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Vо, Ace; Plachkinovа, Miloslava 3 of 3

Abstract

The main purpose of the Sentencing Reform Act of 1984 was to provide more uniformity in sentencing and reduce interjudge disparity. Subsequently, the act created the federal sentencing guidelines to offer judges a possible sentencing range for offenses. However, since these recommendations were based on historical data, the guidelines amplified existing biases and increased inequality and the disproportionate sentencing of minorities. To address this problem, we developed an artifact called “ShowCase”—a data-driven dashboard—that is grounded in penal theory, organizational context theory, social bonds theory, and triangulation notion in design theory. The artifact helps judges make fairer and more objective decisions by integrating a variety of data points. We used a design science research methodology and mixed methods to guide the development and evaluation of the proposed dashboard. Our research inquiry revealed the legal and extralegal factors that contribute to more equitable judicial decisions. We also found support for integrating data science and more diverse viewpoints in the sentencing process. Our study shows that a validated data-driven dashboard can be used to promote fairness, objectivity, and transparency in the criminal justice system. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Journal of the Association for Information Systems. 2023/11, Vol. 24, Issue 6, p1479
  • Document Type:Article
  • Subject Area:Politics and Government
  • Publication Date:2023
  • ISSN:1536-9323
  • DOI:10.17705/1jais.00796
  • Accession Number:173443892
  • Copyright Statement:Copyright of Journal of the Association for Information Systems is the property of Association for Information Systems 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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