Construct Validity

Construct validity assesses whether a measurement accurately reflects the underlying concept it intends to measure. Current research emphasizes improving construct validity across diverse fields, focusing on areas like AI model outputs (e.g., evaluating the validity of text embeddings or reinforcement learning environments), and the design of experiments (e.g., using AI to enhance internal validity in behavioral economics studies or validating causal inferences from observational data). Addressing construct validity is crucial for ensuring the reliability and trustworthiness of research findings and the responsible development and deployment of AI systems in high-stakes applications.