Reproducibility Crisis

The reproducibility crisis in scientific research, particularly within machine learning and artificial intelligence, highlights the difficulty in replicating published findings due to factors like inadequate documentation, insufficient data sharing, and sensitivity to experimental conditions. Current research focuses on improving reproducibility across various domains, including natural language processing, computer vision, and cybersecurity, often examining the impact of specific model architectures (e.g., large language models, deep neural networks) and algorithmic choices on replicability. Addressing this crisis is crucial for ensuring the reliability and trustworthiness of scientific advancements and for fostering the development of robust and dependable AI systems in practical applications.