Simplifying software compliance: AI technologies in drafting technical documentation for the AI Act

The European AI Act has introduced specific technical documentation requirements for AI systems. Compliance with them is challenging due to the need for advanced knowledge of both legal and technical aspects, which is rare among software developers and legal professionals. Consequently, small and medium-sized enterprises may face high costs in meeting these requirements. In this study, we explore how contemporary AI technologies, including ChatGPT and an existing compliance tool (DoXpert), can aid software developers in creating technical documentation that complies with the AI Act. We specifically demonstrate how these AI tools can identify gaps in existing documentation according to the provisions of the AI Act. Using open-source high-risk AI systems as case studies, we collaborated with legal experts to evaluate how closely tool-generated assessments align with expert opinions. Findings show partial alignment, important issues with ChatGPT (3.5 and 4), and a moderate (and statistically significant) correlation between DoXpert and expert judgments, according to the Rank Biserial Correlation analysis. Nonetheless, these findings underscore the potential of AI to combine with human analysis and alleviate the compliance burden, supporting the broader goal of fostering responsible and transparent AI development under emerging regulatory frameworks.

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Publication Details

Journal
Empirical Software Engineering
Published
2025-04-02
DOI
https://doi.org/10.1007/s10664-025-10645-x
Citations
5
Primary Topic
Ethics and Social Impacts of AI
Type
article
Field-Weighted Citation Impact
11.83

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article

Simplifying software compliance: AI technologies in drafting technical documentation for the AI Act

Francesco Sovrano, Alberto Bacchelli, Emmie Hine, Stefano Anzolut
5 citations
Empirical Software Engineering
Ethics and Social Impacts of AI
11.83
article

Simplifying software compliance: AI technologies in drafting technical documentation for the AI Act

Francesco Sovrano, Alberto Bacchelli, Emmie Hine, Stefano Anzolut
article en
5 citations

Abstract

The European AI Act has introduced specific technical documentation requirements for AI systems. Compliance with them is challenging due to the need for advanced knowledge of both legal and technical aspects, which is rare among software developers and legal professionals. Consequently, small and medium-sized enterprises may face high costs in meeting these requirements. In this study, we explore how contemporary AI technologies, including ChatGPT and an existing compliance tool (DoXpert), can aid software developers in creating technical documentation that complies with the AI Act. We specifically demonstrate how these AI tools can identify gaps in existing documentation according to the provisions of the AI Act. Using open-source high-risk AI systems as case studies, we collaborated with legal experts to evaluate how closely tool-generated assessments align with expert opinions. Findings show partial alignment, important issues with ChatGPT (3.5 and 4), and a moderate (and statistically significant) correlation between DoXpert and expert judgments, according to the Rank Biserial Correlation analysis. Nonetheless, these findings underscore the potential of AI to combine with human analysis and alleviate the compliance burden, supporting the broader goal of fostering responsible and transparent AI development under emerging regulatory frameworks.

Empirical Software EngineeringVol. 30(4)
University of Zurich (CH), ETH Zurich (CH), Yale New Haven Health System (US), University of Bologna (IT), KU Leuven (BE)
National Science Foundation, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
Partnerships for the goals
Openalex Percentile: Top 2%
Ethics and Social Impacts of AI
11.83
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Simplifying software compliance: AI technologies in drafting technical documentation for the AI Act — Francesco Sovrano, Alberto Bacchelli, et al. · Empirical Software Engineering (2025) | TGRS Research Map | TGRS