Data Documentation

Data documentation focuses on creating comprehensive and accessible records of datasets used in machine learning, aiming to improve transparency, reproducibility, and responsible AI practices. Current research emphasizes understanding practitioner needs and challenges to design effective and usable documentation frameworks, often exploring how to integrate these frameworks into existing workflows and automate aspects of the process. Improved data documentation is crucial for mitigating biases, enhancing model reliability, and fostering collaboration within the scientific community and across practical applications of machine learning.