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A responsible artificial intelligence framework for forensic science.

Janet Stacey1, Rachel Fleming1, Dion Sheppard1

  • 1Institute of Environmental Science and Research (ESR), 120 Mount Albert Road, Sandringham, Auckland 1025, New Zealand.

Forensic Science International
|July 9, 2025
PubMed
Summary
This summary is machine-generated.

A new Responsible AI Framework (RAIF) addresses the need for detailed operational guidelines in forensic science. This framework supports the safe development and implementation of artificial intelligence (AI) projects, balancing risks and opportunities.

Keywords:
Artificial intelligenceResponsible AI

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Area of Science:

  • Forensic Science
  • Artificial Intelligence
  • Technology Ethics

Background:

  • The increasing integration of artificial intelligence (AI) and automated workflows in forensic science necessitates robust governance.
  • Existing AI guidelines lack the operational detail required for practical implementation within forensic organizations.
  • There is a critical need for a structured approach to ensure AI tools are fit for purpose in forensic applications.

Purpose of the Study:

  • To develop a comprehensive framework for the responsible development and implementation of AI in forensic science.
  • To provide organizations with the necessary tools to operationalize AI governance.
  • To build confidence in the use of AI technologies within the forensic sector.

Main Methods:

  • A review of existing AI guidelines and policies was conducted.
  • A Responsible AI Framework (RAIF) was developed, comprising three key components: a Questionnaire, a Guidelines document, and a Project Register.
  • A worked example applying the RAIF to a specific forensic AI solution (Lumi Drug Scan) was created.

Main Results:

  • The RAIF provides a detailed, actionable structure for managing AI projects in forensic settings.
  • The framework facilitates a balanced approach to leveraging AI opportunities while mitigating associated risks.
  • The RAIF's components, used together, enhance an organization's ability to confidently deploy AI solutions.

Conclusions:

  • The Responsible AI Framework (RAIF) offers a practical solution to the operational challenges of implementing AI in forensic science.
  • The RAIF promotes safe, reliable, and ethical AI deployment, crucial for maintaining public trust and scientific integrity.
  • The framework is adaptable and applicable to various AI projects within forensic organizations, as demonstrated by the Lumi Drug Scan example.