Cyber Security Requirements for Platforms Enhancing AI Reproducibility
This addresses security challenges for AI researchers and developers, but it is incremental as it builds on existing reproducibility concerns with a new evaluation framework.
The study tackled the problem of insufficient cyber security in AI reproducibility platforms by evaluating five popular platforms and finding that none fully met necessary security measures, with Kaggle and Codalab performing better.
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.