Register today to join the webinar organized by the ISWGHS and the Global Network. We are happy to welcome Stephanie Eckman of the Social Data Science Center at the University of Maryland who will present on the topic of "The Science of Data Collection: Insights from Surveys can Improve Machine Learning Models".
Thursday, 30 May 2024, 08:30-09:30 AM NY time (check your time)
Whether future AI models make the world safer or less safe for humans rests in part on our ability to efficiently collect accurate data from people about what they want the models to do. However, collecting high quality data is difficult, and most AI/ML researchers are not trained in data collection methods. The growing emphasis on data-centric AI highlights the potential of data to enhance model performance. It also reveals an opportunity to gain insights from survey methodology, the science of collecting high-quality survey data.
This webinar will summarize lessons from the survey methodology literature and discuss how they can improve the quality of training and feedback data, which in turn improve model performance. Based on the cognitive response process model, the presenter formulates specific hypotheses about the aspects of label collection that may impact training data quality. She will also suggest collaborative research ideas into how possible biases in data collection can be mitigated, making models more accurate and human-centric.
The Speaker
Stephanie works at the Social Data Science Center of the University of Maryland as a Principal Research Scientist specializing in research into data quality and data collection methods. She holds a PhD in Survey Methodology from the University of Maryland. Her research interests include sampling methods in challenging environments and the combination of survey and other forms of data. She has taught sampling and designed samples around the world.
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