  Whether future AI models are fair, trustworthy, and aligned with the public's
interests rests in part on our ability to collect accurate data about what we
want the models to do. However, collecting high-quality data is difficult, and
few AI/ML researchers are trained in data collection methods. Recent research
in data-centric AI has show that higher quality training data leads to better
performing models, making this the right moment to introduce AI/ML researchers
to the field of survey methodology, the science of data collection. We
summarize insights from the survey methodology literature and discuss how they
can improve the quality of training and feedback data. We also suggest
collaborative research ideas into how biases in data collection can be
mitigated, making models more accurate and human-centric.
