Towards Open Standards for Systemic Complexity in Digital Forensics
This work addresses error mitigation in digital forensics for forensic practitioners, but it appears incremental as it builds on existing standards without introducing a new method.
The paper tackles the problem of errors in digital forensics by addressing systemic complexity through human-readable artifacts and open standards, outlining a state-of-the-art AI model schema.
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.