# Towards Open Standards for Systemic Complexity in Digital Forensics

arXiv: https://arxiv.org/abs/2512.12970 · HTML: https://mlsanity.com/paper/2512.12970/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 44 references that are in the map.
- First posted on arXiv 15 Dec 2025.

## Builds on (most cited first, 6 of 6)

- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [A Survey on Causal Inference](https://mlsanity.com/paper/2002.02770/) (2020)
- [Evaluating Code Readability and Legibility: An Examination of Human-centric Studies](https://mlsanity.com/paper/2110.00785/) (2021)
- [A Comprehensive Analysis of the Role of Artificial Intelligence and Machine Learning in Modern Digital Forensics and Incident Response](https://mlsanity.com/paper/2309.07064/) (2023)
- [Research trends, challenges, and emerging topics of digital forensics: A review of reviews](https://mlsanity.com/paper/2108.04634/) (2021)
- [Integrating Fuzzy Logic with Causal Inference: Enhancing the Pearl and Neyman-Rubin Methodologies](https://mlsanity.com/paper/2406.13731/) (2024)

## 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.