Cyber Security Requirements for Platforms Enhancing AI Reproducibility
- URL: http://arxiv.org/abs/2309.15525v1
- Date: Wed, 27 Sep 2023 09:43:46 GMT
- Title: Cyber Security Requirements for Platforms Enhancing AI Reproducibility
- Authors: Polra Victor Falade
- Abstract summary: This study focuses on the field of artificial intelligence (AI) and introduces a new framework for evaluating AI platforms.
Five popular AI platforms; Floydhub, BEAT, Codalab, Kaggle, and OpenML were assessed.
The analysis revealed that none of these platforms fully incorporates the necessary cyber security measures.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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