A responsible artificial intelligence framework for forensic science.

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.