What is Reproducibility in Artificial Intelligence and Machine Learning Research?

TL;DR

This paper clarifies key validation concepts in AI/ML research, proposing a structured framework to address the reproducibility crisis and improve research reliability.

Contribution

It introduces a comprehensive framework that defines and differentiates validation efforts like repeatability and reproducibility in AI/ML.

Findings

Clarifies roles of validation types in AI/ML research

Provides a structured framework for validation efforts

Aims to enhance research reliability and trustworthiness

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.