Towards Open Standards for Systemic Complexity in Digital Forensics
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 6 of its 44 references that are in the map. First posted on arXiv 15 Dec 2025.
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6 of 44 references.
References · 6
- 2018Model Cards for Model Reportingcited by 1,068
- 2020A Survey on Causal Inferencecited by 202
- 2021Evaluating Code Readability and Legibility: An Examination of Human-centric Studiescited by 8
- 2023A Comprehensive Analysis of the Role of Artificial Intelligence and Machine Learning in Modern Digital Forensics and Incident Responsecited by 8
- 2021Research trends, challenges, and emerging topics of digital forensics: A review of reviewscited by 5
- 2024Integrating Fuzzy Logic with Causal Inference: Enhancing the Pearl and Neyman-Rubin Methodologiescited by 1
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