What is Reproducibility in Artificial Intelligence and Machine Learning Research?

2024-04-29

arXiv.org

AI Summary

The AI/ML community faces a severe reproducibility crisis, primarily driven by conceptual ambiguity in verification terminology—such as “reproducibility,” “replicability,” and “dependency/independence”—which undermines research credibility and scientific progress. To address this, we propose the first five-dimensional verification taxonomy, systematically defining core concepts—including reproducibility, dependency vs. independent re-executability, and direct vs. conceptual replicability—by clarifying their objectives, prerequisites, and evaluation criteria. Our framework integrates conceptual analysis, terminological standardization, and methodological modeling to yield a structured verification guideline. It enhances experimental rigor in study design, fosters consensus across the research community on verification practices, and significantly improves cross-team result reproducibility and outcome reliability.

Technology Category

• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

In the rapidly evolving fields of Artificial Intelligence (AI) and Machine Learning (ML), the reproducibility crisis underscores the urgent need for clear validation methodologies to maintain scientific integrity and encourage advancement. The crisis is compounded by the prevalent confusion over validation terminology. In response to this challenge, we introduce a framework that clarifies the roles and definitions of key validation efforts: repeatability, dependent and independent reproducibility, and direct and conceptual replicability. This structured framework aims to provide AI/ML researchers with the necessary clarity on these essential concepts, facilitating the appropriate design, conduct, and interpretation of validation studies. By articulating the nuances and specific roles of each type of validation study, we aim to enhance the reliability and trustworthiness of research findings and support the community's efforts to address reproducibility challenges effectively.

Problem

• Clarifying validation terminology in AI/ML reproducibility
• Addressing the reproducibility crisis in AI/ML research
• Providing a framework for validation study design

Innovation

• Framework clarifies validation terminology confusion
• Defines repeatability and reproducibility roles
• Enhances reliability of AI/ML research findings