  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. Responding to this challenge, we introduce a validation
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 hope to contribute to a more informed and methodical
approach to addressing the challenges of reproducibility, thereby supporting
the community's efforts to enhance the reliability and trustworthiness of its
research findings.
