人工智能与死者身份鉴定:法医学的叙事回顾。

IF 1 3区 社会学 Q2 LAW
Damini Siwan, Akansha Rana, Peehul Krishan, Vishal Sharma, Kewal Krishan
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引用次数: 0

摘要

在大规模灾难、战争罪行和法医检查中,死者的身份识别至关重要。法医人类学家建立的生物侧写是确认死者身份的必要步骤之一。一些参数可以估计,如性别,年龄,身高,生物地理亲缘关系,和DNA谱未知的人。估计这些可能缩小调查过程的鉴定参数是至关重要的。另一方面,现代世界的人工智能(AI)在不同领域显示出神奇的用途。本交流旨在强调人工智能工具的使用,以更准确、更短的时间预测未知人员的性别、年龄、身高、生物地理亲和力和DNA图谱等参数。利用PubMed、Scopus、Web of Science和ScienceDirect等数据库进行文献检索,分析人工智能、机器学习和深度学习算法在灾害受害者识别(DVI)和法医案件工作中建立生物图谱的使用情况。此外,这项研究预测,随着技术的进步,调查技术将发生范式转变,突出人工智能和人类学思想的融合,以提高对未知死者生物特征的理解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Artificial Intelligence and Identification of the Deceased: a Narrative Review With Implications in Forensic Science.

Identification of the dead is of utmost importance in mass disasters, war crimes, and forensic examinations. The biological profile, established by a forensic anthropologist is one the necessary steps involved in the identification of the dead. Several parameters can be estimated such as sex, age, stature, biogeographical affinity, and DNA profile of the unknown person. It is crucial to estimate these parameters of identification which may narrow down the investigation process. On the other hand, Artificial Intelligence (AI) in the modern world is showing magical uses in different fields. This communication aims to highlight the uses of AI tools for predicting parameters such as sex, age, stature, biogeographical affinity, and DNA profile of unknown persons with more accuracy and in less time. A literature search was conducted using databases PubMed, Scopus, Web of Science, and ScienceDirect for analyzing the use of artificial intelligence, machine learning, and deep learning algorithms for establishing the biological profile in disaster victim identification (DVI) and forensic casework. Moreover, this research foresees a paradigm shift in investigative techniques as technology advances, highlighting the convergence of AI and anthropological ideas for an improved understanding of the biological profiles of unknown deceased individuals.

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来源期刊
CiteScore
2.50
自引率
7.10%
发文量
50
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