Paper Title

Harnassing AI in Criminal Justice: Transforming Predictive Policing and Forensic Evidence Analysis

Article Identifiers

Registration ID: IJNRD_227265

Published ID: IJNRD2408416

DOI: Click Here to Get

Authors

Komal Goswami , Malavika Murali

Keywords

AI Algorithms, Bias Mitigation, Criminal Justice Ethics, Forensic Evidence Analysis, Predictive Policing.

Abstract

Artificial intelligence (AI) has become a significant factor in the criminal justice system, particularly in predictive policing and forensic evidence analysis. Predictive policing is the use of AI algorithms to analyse previous crime data, detecting patterns and trends that assist law enforcement organisations in forecasting where future crimes are likely to occur. This proactive method strives to optimise police resource allocation, reduce crime, and improve public safety. The promise of predictive policing stems from its ability to reduce crime rates through data-driven initiatives. However, this technology raises serious issues about privacy, civil liberties, and the possibility of reinforcing existing biases in policing procedures. AI algorithms in predictive policing frequently rely on data that may reflect historical preconceptions, resulting in biased decisions. For example, if some neighbourhoods are overrepresented in the data, the AI model may unfairly target those places, repeating a cycle of over policing and mistrust. To solve these concerns, it is critical to create effective bias detection and mitigation approaches, enhance AI algorithm transparency, and retain human oversight to prevent discriminatory practices. In addition to predictive policing, AI has transformed forensic evidence analysis, increasing the accuracy and efficiency of investigations. AI-powered systems can analyse complicated data sets, such as DNA, fingerprints, and digital evidence, with greater accuracy than traditional approaches. AI algorithms, for example, can quickly evaluate massive amounts of DNA samples, discovering matches and offering crucial leads in criminal investigations. Similarly, AI can help with digital forensics by analyzing massive amounts of digital data like emails, social media activity, and electronic transactions to find evidence of criminal conduct. Artificial intelligence offers substantial advantages in forensic evidence analysis. By automating mundane processes and giving complex analytical skills, AI can eliminate human error, speed up case processing, and improve the reliability of forensic evidence. However, integrating AI into forensic analysis raises certain obstacles. Ensuring the legitimacy and reliability of AI-generated evidence is critical, because errors or biases in the analysis could have serious consequences for justice. To ensure fair trial standards, courts must be able to scrutinize AI methodology and comprehend the limitations of AI-generated evidence. Ethical and legal considerations are critical to the use of AI in criminal justice. Establishing clear norms and guidelines is critical for governing the use of AI technologies, protecting individual rights, and maintaining public trust. This includes creating explainable AI models that bring transparency into decision-making processes, as well as adopting rigorous auditing procedures to assure accountability. To summarise, AI shows significant promise for improving predictive policing and forensic evidence analysis in the criminal justice system. However, realizing this promise necessitates careful consideration of the ethical, legal, and societal consequences. By emphasizing openness, accountability, and fairness, the criminal justice system can use AI technology to improve outcomes while upholding the concepts of justice and equity.

How To Cite

"Harnassing AI in Criminal Justice: Transforming Predictive Policing and Forensic Evidence Analysis", IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.9, Issue 8, page no.e181-e192, August-2024, Available :https://ijnrd.org/papers/IJNRD2408416.pdf

Issue

Volume 9 Issue 8, August-2024

Pages : e181-e192

Other Publication Details

Paper Reg. ID: IJNRD_227265

Published Paper Id: IJNRD2408416

Downloads: 000121165

Research Area: Other

Country: Bengaluru, Karnataka, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2408416.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2408416

About Publisher

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

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Call For Paper

Call For Paper - Volume 10 | Issue 8 | August 2025

IJNRD is Scholarly open access journals, Peer-reviewed, and Refereed Journals, High Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool), Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) with Open-Access Publications.

INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (IJNRD) aims to explore advances in research pertaining to applied, theoretical and experimental Technological studies. The goal is to promote scientific information interchange between researchers, developers, engineers, students, and practitioners working in and around the world. IJNRD will provide an opportunity for practitioners and educators of engineering field to exchange research evidence, models of best practice and innovative ideas.

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Paper Submission Open For: August 2025

Current Issue: Volume 10 | Issue 8

Last Date for Paper Submission: Till 31-Aug-2025

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Publication of Paper: Within 01-02 Days after Submititng documents.

Frequency: Monthly (12 issue Annually).

Journal Type: International Peer-reviewed, Refereed, and Open Access Journal.

Subject Category: Research Area