Towards Open Standards for Systemic Complexity in Digital Forensics

TL;DR

This paper discusses developing open standards and human-readable artifacts to address systemic complexity and reduce errors in digital forensics involving AI technologies.

Contribution

It proposes a schema for digital forensics AI models based on current best practices to improve transparency and standardization.

Findings

Identification of systemic complexity in digital forensics

Proposal of a human-readable artifact schema

Emphasis on open standards to reduce errors

Abstract

The intersection of artificial intelligence (AI) and digital forensics (DF) is becoming increasingly complex, ubiquitous, and pervasive, with overlapping techniques and technologies being adopted in all types of scientific and technical inquiry. Despite incredible advances, forensic sciences are not exempt from errors and remain vulnerable to fallibility. To mitigate the limitations of errors in DF, the systemic complexity is identified and addressed with the adoption of human-readable artifacts and open standards. A DF AI model schema based on the state of the art is outlined.