Research Summary: Position: Let's Strengthen Verifiability If We Can't Enforce Reproducibility

Research in Machine Learning (ML) faces significant challenges regarding the reproducibility of empirical results. Many published findings are difficult to replicate due to a lack of available code and supporting materials, hindering scientific progress. This paper analyzes these issues and proposes strengthening the verifiability of research outcomes as a practical alternative when full reproducibility cannot be enforced.

Why it matters

The inability to reliably reproduce research results undermines the foundational principles of scientific inquiry and can impede technological progress. For sectors reliant on ML innovations, this presents a significant risk to the validation and deployment of new methods, potentially leading to wasted investment and misinformed decisions.

Key insights

• Empirical results in Machine Learning papers are generally difficult to reproduce.
• The challenge of reproducibility is increasing over time.
• Lack of accessible code is a primary impediment to result reproduction.
• These issues collectively hinder the development of research in the field.
• Proposals focus on improving the 'checkability' of results if full reproducibility is unattainable.

Source

arXiv — Computers and Society — https://arxiv.org/abs/2609.35854