Scientific research is increasingly reliant on computational methods, posing
challenges for ensuring research reproducibility. This study focuses on the
field of artificial intelligence (AI) and introduces a new framework for
evaluating AI platforms for reproducibility from a cyber security standpoint to
address the security challenges associated with AI research. Using this
framework, five popular AI reproducibility platforms; Floydhub, BEAT, Codalab,
Kaggle, and OpenML were assessed. The analysis revealed that none of these
platforms fully incorporates the necessary cyber security measures essential
for robust reproducibility. Kaggle and Codalab, however, performed better in
terms of implementing cyber security measures covering aspects like security,
privacy, usability, and trust. Consequently, the study provides tailored
recommendations for different user scenarios, including individual researchers,
small laboratories, and large corporations. It emphasizes the importance of
integrating specific cyber security features into AI platforms to address the
challenges associated with AI reproducibility, ultimately advancing
reproducibility in this field. Moreover, the proposed framework can be applied
beyond AI platforms, serving as a versatile tool for evaluating a wide range of
systems and applications from a cyber security perspective.

通过一个从网络安全角度评估人工智能平台的可再现性的框架，该研究发现目前流行的 AI 可再现性平台 Floydhub、BEAT、Codalab、Kaggle 和 OpenML 中没有一个完全整合了所需的网络安全措施，而 Kaggle 和 Codalab 在安全性、隐私性、易用性和可信度方面表现较好。因此，该研究针对不同用户场景提供了个性化建议，包括个人研究人员、小型实验室和大型公司，强调将特定的网络安全功能整合到 AI 平台中以解决与 AI 可再现性相关的挑战，从而推动该领域的可再现性。此外，该提出的框架还可以应用于其他领域的系统和应用程序的网络安全评估。