Leveraging Generative AI to Improve the Survey Process: Use Cases and Challenges

Music City Center 

Artificial intelligence (AI), both traditional and generative, has already been adopted in the federal system. Agencies at the federal and local levels have started using AI-driven methods for evidence-based policy, to process and extract insights from text documents (such as application forms), and to develop predictive machine learning models. AI-driven technologies help automate and streamline processes, reduce administrative burdens, and reach decisions more accurately, consistently, and quickly. The broad span of AI use cases starts from questionnaire design and translation to open-ended coding and analysis. AI has also taken place in the federal statistical system, and agencies have started using AI to enhance and improve surveys. AI methods, specifically machine learning and NLP, can be used broadly throughout the survey process, including data collection, processing, imputation, dissemination, and analysis, as well as addressing data confidentiality and disclosure avoidance. In this presentation, how AI can enhance the survey process by improving data quality and generating efficiencies will be summarized. The limitations and challenges associated with these methods and strategies to mitigate these concerns will be addressed.