Surveying Attitudinal Alignment

Surveying attitudinal alignment focuses on measuring and comparing the attitudes of different entities, such as humans and AI models, towards specific issues or concepts. Current research employs diverse methods, including text mining, latent class choice models incorporating artificial neural networks, and comparative analyses of open-ended versus closed-ended survey responses, to understand these attitudinal differences. This work is significant for improving the design of AI systems, optimizing human-computer interaction, and informing policy decisions across various domains, from sustainable development to talent management. The ability to accurately gauge and align attitudes is crucial for building more effective and ethically sound systems.